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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-12-04
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">138</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">12</span>
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                <span id="display-count" class="font-medium">显示 138 篇论文 (共 138 篇)</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04009v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>学习比较购物
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Learning to Comparison-Shop
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jie Tang, Daochen Zha, Xin Liu, Huiji Gao, Liwei He, Stephanie Moyerman, Sanjeev...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究在线市场（如Airbnb）中用户比较购物行为与现有排序模型的脱节问题。其核心方法是提出学习比较购物（LTCS）系统，通过显式建模和学习用户在搜索结果页面上比较多个商品的行为来改进排序。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统/搜索中的核心排序问题，提出建模用户比较购物行为的新架构，与核心领域进展和直接LLM应用高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:46:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04009v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04009v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In online marketplaces like Airbnb, users frequently engage in comparison shopping before making purchase decisions. Despite the prevalence of this behavior, a significant disconnect persists between mainstream e-commerce search engines and users' comparison needs. Traditional ranking models often evaluate items in isolation, disregarding the context in which users compare multiple items on a search results page. While recent advances in deep learning have sought to improve ranking accuracy, diversity, and fairness by encoding listwise context, the challenge of aligning search rankings with user comparison shopping behavior remains inadequately addressed. In this paper, we propose a novel ranking architecture - Learning-to-Comparison-Shop (LTCS) System - that explicitly models and learns users' comparison shopping behaviors. Through extensive offline and online experiments, we demonstrate that our approach yields statistically significant gains in key business metrics - improving NDCG by 1.7% and boosting booking conversion rate by 0.6% in A/B testing - while also enhancing user experience. We also compare our model against state-of-the-art approaches and demonstrate that LTCS significantly outperforms them.
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<div class="paper-card p-4 expanded">
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            <a href="https://www.alphaxiv.org/abs/2512.03870v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>通过跨层融合重构键值缓存以增强Transformer模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Reconstructing KV Caches with Cross-layer Fusion For Enhanced Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongzhan Lin, Zhiqi Bai, Xinmiao Zhang, Sen Yang, Xiang Li, Siran Yang, Yunlong ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究Transformer解码器中KV缓存内存占用过高的问题。其核心方法是分析不同层对键值信息的贡献分布，提出FusedKV机制，通过可学习的跨层融合（结合底层值和中层键）重构顶层KV缓存，在保留位置信息的同时显著减少内存需求。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对Transformer架构的KV缓存效率瓶颈提出创新解决方案，通过跨层融合机制优化内存使用，属于Transformer技术进展的核心领域，对推荐/搜索系统的长序列处理有直接应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 15:22:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03870v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03870v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Transformer decoders have achieved strong results across tasks, but the memory required for the KV cache becomes prohibitive at long sequence lengths. Although Cross-layer KV Cache sharing (e.g., YOCO, CLA) offers a path to mitigate KV Cache bottleneck, it typically underperforms within-layer methods like GQA. To understand the root cause, we investigate the information flow of keys and values of the top-layers. Our preliminary reveals a clear distribution: values are predominantly derived from the bottom layer, while keys draw more information from both bottom and middle layers. Building upon this, we propose FusedKV, whose top-layer KV caches are a learnable fusion of the most informative ones from the bottom and middle layers. This fusion operates directly on post-RoPE keys, preserving relative positional information without the computational cost of re-applying rotary embeddings. To further improve efficiency, we propose FusedKV-Lite, an cross-layer sharing approach, where top-layer KV caches are directly derived from the bottom-layer values and the middle-layer keys. Compared to FusedKV, FusedKV-Lite reduces I/O overhead at the cost of a slight increase in perplexity. In experiments on LLMs ranging from 332M to 4B parameters, our proposed method reduce 50\% cache memory while achieving lower validation perplexity than the standard Transformer decoder, establishing it as a memory-efficient, high-performance architectural alternative.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03494v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>原生Top-k稀疏注意力机制的潜力与挑战初步研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            A Preliminary Study on the Promises and Challenges of Native Top-$k$ Sparse Attention
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Di Xiu, Hongyin Tang, Bolin Rong, Lizhi Yan, Jingang Wang, Yifan Lu, Xunliang Ca...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何通过Top-k稀疏注意力机制降低LLM在长上下文建模中的计算成本。其核心思想是在解码和训练阶段都采用精确或近似的Top-k注意力，仅保留与查询最相关的关键键值对，并通过训练-推理一致性策略及熵减理论来优化该方法的有效性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究Transformer核心组件注意力机制的稀疏化优化，属于Transformer架构效率提升的关键技术，对大规模推荐和搜索系统的推理加速有直接应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 06:44:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03494v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03494v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are increasingly prevalent in the field of long-context modeling, however, their inference computational costs have become a critical bottleneck hindering the advancement of tasks such as agents and multimodal applications. This report conducts a preliminary investigation into the effectiveness and theoretical mechanisms of the Top-$k$ Attention mechanism during both the decoding and training phases. First, we validate the effectiveness of exact Top-$k$ Decoding through extensive experimentation. Experiments demonstrate that retaining only the pivotal Keys with the highest similarity to the Query as the context window during the decoding stage achieves performance comparable to, or even surpassing, full attention on downstream tasks such as HELMET and LongBench v2. Second, we further explore the native Top-$k$ Attention training strategy. Experiments confirm that ensuring the consistency between training and inference regarding Top-$k$ Attention operations facilitates the further unlocking of Top-$k$ Decoding's potential, thereby significantly enhancing model performance. Furthermore, considering the high computational complexity of exact Top-$k$ Attention, we investigate the impact of approximate Top-$k$ algorithm precision on downstream tasks. Our research confirms a positive correlation between downstream task performance and approximation fidelity, and we provide statistical evaluations of the Lightning Indexer's precision within the DeepSeek-V3.2-Exp model. Finally, this report provides a theoretical interpretation from the perspective of Entropy. Experimental observations indicate that models subjected to Top-$k$ Attention SFT exhibit a distinct phenomenon of entropy reduction in downstream tasks, which validates the hypothesis that low-entropy states are better adapted to Top-$k$ Decoding.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03343v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>思想门控Transformer：通过可微分词汇剪枝实现语义连贯性
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Idea-Gated Transformers: Enforcing Semantic Coherence via Differentiable Vocabulary Pruning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Darshan Fofadiya
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">主题：解决自回归语言模型因下一词预测目标导致的主题漂移问题。核心思想：提出Idea-Gated Transformer架构，通过辅助的“Idea Head”预测未来上下文窗口的词袋分布，生成“概念向量”，并利用可微门控机制实时抑制语义无关的词汇，实现语义规划与语法生成的分离。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的Idea-Gated Transformer通过分离语义规划与语法生成，并引入可微词汇门控机制，直接针对LLM生成中的主题漂移问题，其核心架构创新对提升推荐、搜索等系统中内容生成的可控性与语义一致性具有直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 01:17:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03343v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03343v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Autoregressive Language Models (LLMs) trained on Next-Token Prediction (NTP) often suffer from ``Topic Drift'' where the generation wanders away from the initial prompt due to a reliance on local associations rather than global planning \citep{holtzman2019curious}. While scaling model size mitigates this \citep{brown2020language}, the fundamental myopia of the NTP objective remains. In this work, we introduce the Idea-Gated Transformer, a novel architecture that separates semantic planning from syntactic generation. We introduce an auxiliary ``Idea Head'' trained to predict the bag-of-words distribution for a future context window, creating a latent ``Concept Vector'' that actively gates the main vocabulary during generation. We propose a differentiable gating mechanism that suppresses semantically irrelevant tokens, effectively pruning the search space in real-time. Experiments on WikiText-103 demonstrate that while the Idea-Gated model achieves comparable validation perplexity to a standard GPT-2 baseline, it exhibits significantly superior Domain Retention. Qualitative and quantitative analysis reveals that the gating mechanism successfully locks generation into specific semantic clusters (e.g., Finance, Science) and resists associative drift, offering a parameter-efficient path toward more controllable language modeling.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03439v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>LLM作为推荐系统的可解释性重排序器
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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        <div class="mb-2 text-base text-gray-700">
            LLM as Explainable Re-Ranker for Recommendation System
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaqi Wang, Haojia Sun, Shuting Zhang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何解决传统推荐系统缺乏可解释性和存在流行度偏差的问题。其核心思想是提出一种混合方法，将传统推荐模型与LLM结合，利用LLM作为可解释的重排序器来提升推荐结果的可解释性和准确性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接应用LLM作为推荐系统的可解释重排序器，属于LLM在推荐领域的直接应用范畴，并关注可解释性这一核心问题。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 04:42:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03439v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03439v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The application of large language models (LLMs) in recommendation systems has recently gained traction. Traditional recommendation systems often lack explainability and suffer from issues such as popularity bias. Previous research has also indicated that LLMs, when used as standalone predictors, fail to achieve accuracy comparable to traditional models. To address these challenges, we propose to use LLM as an explainable re-ranker, a hybrid approach that combines traditional recommendation models with LLMs to enhance both accuracy and interpretability. We constructed a dataset to train the re-ranker LLM and evaluated the alignment between the generated dataset and human expectations. Leveraging a two-stage training process, our model significantly improved NDCG, a key ranking metric. Moreover, the re-ranker outperformed a zero-shot baseline in ranking accuracy and interpretability. These results highlight the potential of integrating traditional recommendation models with LLMs to address limitations in existing systems and pave the way for more explainable and fair recommendation frameworks.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03989v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>让旧分词器学习新词汇：预训练模型的高效分词器适配
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Teaching Old Tokenizers New Words: Efficient Tokenizer Adaptation for Pre-trained Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taido Purason, Pavel Chizhov, Ivan P. Yamshchikov, Mark Fishel
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究预训练模型在迁移到新领域或语言时，如何高效地扩展和精简分词器词汇表。其核心方法是：通过继续BPE合并学习过程来适应新数据，以及基于叶子节点的词汇剪枝技术，从而在不影响模型质量的前提下优化分词器。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出高效的自适应分词器方法，直接提升LLM在特定领域（如推荐、搜索）的词汇覆盖和效率，属于核心LLM技术进展。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:20:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03989v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03989v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Tokenizer adaptation plays an important role in transferring pre-trained language models to new domains or languages. In this work, we address two complementary aspects of this process: vocabulary extension and pruning. The common approach to extension trains a new tokenizer on domain-specific text and appends the tokens that do not overlap with the existing vocabulary, which often results in many tokens that are unreachable or never used. We propose continued BPE training, which adapts a pre-trained tokenizer by continuing the BPE merge learning process on new data. Experiments across multiple languages and model families show that this approach improves tokenization efficiency and leads to better utilization of added vocabulary. We also introduce leaf-based vocabulary pruning, which removes redundant tokens while preserving model quality. Together, these methods provide practical tools for controlled vocabulary modification, which we release as an open-source package.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03402v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>双LoRA：通过幅度与方向更新增强LoRA
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Dual LoRA: Enhancing LoRA with Magnitude and Direction Updates
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yixing Xu, Chao Li, Xuanwu Yin, Spandan Tiwari, Dong Li, Ashish Sirasao, Emad Ba...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何提升LoRA在预训练大语言模型下游任务微调中的性能。其核心思想是将低秩矩阵分解为幅度组和方向组，分别控制参数更新的幅度和方向，以更好地模拟基于梯度的全参数微调过程。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出Dual LoRA方法，通过引入幅度和方向更新机制来增强LoRA，直接改进Transformer架构的高效微调技术，对RecSys和Ads中的LLM应用具有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 03:14:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03402v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03402v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Low-rank adaptation (LoRA) is one of the most popular methods among parameter-efficient fine-tuning (PEFT) methods to adapt pre-trained large language models (LLMs) to specific downstream tasks. However, the model trained based on LoRA often has an unsatisfactory performance due to its low-rank assumption. In this paper, we propose a novel method called Dual LoRA to improve the performance by incorporating an inductive bias into the original LoRA. Specifically, we separate low-rank matrices into two groups: the magnitude group to control whether or not and how far we should update a parameter and the direction group to decide whether this parameter should move forward or backward, to better simulate the parameter updating process of the full fine-tuning based on gradient-based optimization algorithms. We show that this can be simply achieved by adding a ReLU function to the magnitude group and a sign function to the direction group. We conduct several experiments over a wide range of NLP tasks, including natural language generation (NLG), understanding (NLU), and commonsense reasoning datasets on GPT-2, RoBERTa, DeBERTa, and LLaMA-1/2/3 as baseline models. The results show that we consistently outperform LoRA and its state-of-the-art variants with the same number of trainable parameters.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03377v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Nexus：Transformer中的高阶注意力机制
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Nexus: Higher-Order Attention Mechanisms in Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hanting Chen, Chu Zhong, Kai Han, Yuchuan Tian, Yuchen Liang, Tianyu Guo, Xingha...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究标准Transformer注意力机制因低秩瓶颈难以捕捉复杂多跳关系的问题。其核心方法是提出高阶注意力网络，通过递归框架让查询和键向量先经过内部注意力循环聚合全局上下文，从而在最终注意力计算前建模高阶相关性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出高阶注意力机制，直接改进Transformer核心架构，属于Transformer技术进展范畴，对推荐、搜索、广告系统的模型效率有重要应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 02:25:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03377v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03377v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Transformers have achieved significant success across various domains, relying on self-attention to capture dependencies. However, the standard first-order attention mechanism is often limited by a low-rank bottleneck, struggling to capture intricate, multi-hop relationships within a single layer. In this paper, we propose the \textbf{Higher-Order Attention Network (Hon)}, a novel architecture designed to enhance representational power through a recursive framework. Unlike standard approaches that use static linear projections for Queries and Keys, Hon dynamically refines these representations via nested self-attention mechanisms. Specifically, the Query and Key vectors are themselves outputs of inner attention loops, allowing tokens to aggregate global context and model high-order correlations \textit{prior} to the final attention computation. We enforce a parameter-efficient weight-sharing strategy across recursive steps, ensuring that this enhanced expressivity incurs $\mathcal{O}(1)$ additional parameters. We provide theoretical analysis demonstrating that our method breaks the linear bottleneck of standard attention. Empirically, Hon outperforms standard Transformers on multiple benchmarks.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04025v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>PSA：金字塔稀疏注意力机制，用于高效视频理解与生成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            PSA: Pyramid Sparse Attention for Efficient Video Understanding and Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaolong Li, Youping Gu, Xi Lin, Weijie Wang, Bohan Zhuang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">主题：解决注意力机制二次复杂度带来的计算瓶颈问题，特别是在高稀疏度下现有方法因二进制掩码导致信息丢失严重。核心思想：提出金字塔稀疏注意力（PSA），通过为每个查询块动态分配不同池化级别的键值表示，而非简单的保留或丢弃，从而在计算效率和信息保留之间实现更优的权衡。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种新型的稀疏注意力机制，直接针对Transformer架构的效率瓶颈进行优化，其核心思想——通过多级池化KV表示实现精细粒度掩码——在推荐、搜索和广告系统中处理长序列或高维特征时具有直接的应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:02:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04025v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04025v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Attention mechanisms are the core of foundation models, but their quadratic complexity remains a critical bottleneck for scaling. This challenge has driven the development of efficient attention mechanisms, with sparsity emerging as the dominant paradigm. Current methods typically retain or discard entire key-value blocks with binary masks, resulting in substantial information loss under high sparsity. To mitigate this gap, we present Pyramid Sparse Attention (PSA), a versatile module applicable to both video understanding and generation tasks. Instead of binary masking, PSA introduces multi-level pooled KV representations, enabling finer mask granularity. Specifically, each query block dynamically allocates lower pooling levels to critical KV blocks and higher levels to less important ones, creating an informative interpolation between full retention and complete pruning. This design, analogous to fixed-point quantization and classical feature pyramid networks in computer vision, effectively mitigates information loss while preserving computational efficiency under a low compute budget. It works with a native, hardware-friendly kernel that leverages decoupled block-tile design to ensure efficient execution. Across video understanding and generation benchmarks, PSA preserves contextual information and visual fidelity, consistently outperforming or achieving comparable performance over existing sparse attention baselines with superior efficiency-quality trade-offs. Our code and model weights are publicly available at: http://ziplab.co/PSA
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03963v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>TempR1：通过时序感知多任务强化学习提升多模态大语言模型的时序理解能力
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tao Wu, Li Yang, Gen Zhan, Yiting Liao, Junlin Li, Deliang Fu, Li Zhang, Limin W...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何提升多模态大语言模型对视频等时序数据的理解能力。其核心方法是设计一个时序感知的多任务强化学习框架，通过将时序任务按预测区间与真实实例的对应关系分为三类，并为每类任务设计定制化的定位奖励，从而系统性地增强模型对细粒度时序依赖和不同时序模式的建模能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出一种基于多任务强化学习的框架，专门增强多模态大语言模型对时序信息的理解能力，其核心方法（任务分类与定制化奖励设计）与架构优化思路对推荐/搜索系统中的序列建模与时间感知任务具有直接借鉴价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 16:57:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03963v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03963v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Enhancing the temporal understanding of Multimodal Large Language Models (MLLMs) is essential for advancing long-form video analysis, enabling tasks such as temporal localization, action detection, and time-sensitive question answering. While reinforcement learning (RL) has recently been explored for improving temporal reasoning, existing approaches are often confined to limited task types and data, restricting their generalization across diverse temporal understanding scenarios. To address this challenge, we present TempR1, a temporal-aware multi-task reinforcement learning framework that systematically strengthens MLLMs' temporal comprehension. We curate a multi-task corpus that exposes the model to diverse temporal structures and semantics, and build upon the Group Relative Policy Optimization (GRPO) algorithm to achieve stable and effective cross-task optimization. Specifically, we categorize temporal tasks into three correspondence types between predicted intervals and ground-truth instances, and design tailored localization rewards for each, enabling TempR1 to capture fine-grained temporal dependencies and adapt to different temporal patterns. Extensive experiments demonstrate that TempR1 attains state-of-the-art performance across multiple benchmarks. Moreover, its joint optimization over complementary tasks yields a strong synergistic effect, enhancing both generalization and single-task performance, establishing a scalable and principled paradigm for temporal reasoning in MLLMs.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04013v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>AugServe：面向增强型大语言模型推理服务的自适应请求调度
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            AugServe: Adaptive Request Scheduling for Augmented Large Language Model Inference Serving
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ying Wang, Zhen Jin, Jiexiong Xu, Wenhai Lin, Yiquan Chen, Wenzhi Chen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何提升增强型大语言模型推理服务的效率。其核心思想是设计一个两阶段自适应请求调度策略，通过结合请求特征和运行时信息动态优化调度顺序与批处理机制，以应对负载波动和硬件变化。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于增强型LLM推理服务的高效调度，属于LLM技术在实际应用中的系统优化，与推荐/搜索/广告系统的性能需求直接相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:49:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04013v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04013v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As augmented large language models (LLMs) with external tools become increasingly popular in web applications, improving augmented LLM inference serving efficiency and optimizing service-level objectives (SLOs) are critical for enhancing user experience. To achieve this, inference systems must maximize request handling within latency constraints, referred to as increasing effective throughput. However, existing systems face two major challenges: (i) reliance on first-come-first-served (FCFS) scheduling causes severe head-of-line blocking, leading to queuing delays exceeding the SLOs for many requests; and (ii) static batch token limit, which fails to adapt to fluctuating loads and hardware conditions. Both of these factors degrade effective throughput and service quality. This paper presents AugServe, an efficient inference framework designed to reduce queueing latency and enhance effective throughput for augmented LLM inference services. The core idea of AugServe is a two-stage adaptive request scheduling strategy. Specifically, AugServe combines the inference features of augmented LLM requests to optimize the order of scheduling decisions (stage I). These decisions are continuously refined with runtime information (stage II), adapting to both request characteristics and system capabilities. In addition, AugServe dynamically adjusts the token batching mechanism based on hardware status and real-time load, further enhancing throughput performance. Experimental results show that AugServe achieves 4.7-33.1x and 3.3-13.2x higher effective throughput than vLLM and InferCept, while reducing time-to-first-token (TTFT) by up to 96.3% and 95.0%, respectively.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03862v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>自监督学习中的收益递减现象
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Diminishing Returns in Self-Supervised Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Oli Bridge, Huey Sun, Botond Branyicskai-Nagy, Charles D'Ornano, Shomit Basu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究了小型视觉Transformer在不同预训练、中间微调和下游任务中的性能边际效益问题。核心发现是预训练和微调存在收益递减现象，且不恰当的中间任务堆叠可能因任务机制差异而损害下游性能。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究了预训练和微调对小型视觉Transformer的边际效益，虽然直接涉及Transformer架构和训练策略，但主要关注计算机视觉领域，与推荐、搜索或广告系统的直接应用关联较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 15:11:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03862v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03862v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While transformer-based architectures have taken computer vision and NLP by storm, they often require a vast amount of parameters and training data to attain strong performance. In this work, we experiment with three distinct pre-training, intermediate fine-tuning, and downstream datasets and training objectives to explore their marginal benefits on a small 5M-parameter vision transformer. We find that while pre-training and fine-tuning always help our model but have diminishing returns, intermediate fine-tuning can actually show harmful impact on downstream performance, potentially due to dissimilarity in task mechanics. Taken together, our results suggest that small-scale ViTs benefit most from targeted pre-training and careful data selection, while indiscriminate stacking of intermediate tasks can waste compute and even degrade performance.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03514v1" target="_blank" rel="noopener noreferrer">
                M3DR：迈向通用多语言多模态文档检索
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            M3DR: Towards Universal Multilingual Multimodal Document Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Adithya S Kolavi, Vyoman Jain
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注多语言多模态文档检索，属于检索领域的核心进展，与搜索相关。然而，标题未明确提及LLM或Transformer技术，也未涉及推荐或广告系统，且多模态可能包含视觉等与当前焦点无关的模态，因此相关性有限。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 07:17:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03514v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03514v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal document retrieval systems have shown strong progress in aligning visual and textual content for semantic search. However, most existing approaches remain heavily English-centric, limiting their effectiveness in multilingual contexts. In this work, we present M3DR (Multilingual Multimodal Document Retrieval), a framework designed to bridge this gap across languages, enabling applicability across diverse linguistic and cultural contexts. M3DR leverages synthetic multilingual document data and generalizes across different vision-language architectures and model sizes, enabling robust cross-lingual and cross-modal alignment. Using contrastive training, our models learn unified representations for text and document images that transfer effectively across languages. We validate this capability on 22 typologically diverse languages, demonstrating consistent performance and adaptability across linguistic and script variations. We further introduce a comprehensive benchmark that captures real-world multilingual scenarios, evaluating models under monolingual, multilingual, and mixed-language settings. M3DR generalizes across both single dense vector and ColBERT-style token-level multi-vector retrieval paradigms. Our models, NetraEmbed and ColNetraEmbed achieve state-of-the-art performance with ~150% relative improvements on cross-lingual retrieval.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03413v1" target="_blank" rel="noopener noreferrer">
                BookRAG：一种基于层次结构感知索引的复杂文档检索增强生成方法
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shu Wang, Yingli Zhou, Yixiang Fang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注检索增强生成（RAG）在复杂文档上的应用，属于LLM技术的基础应用层面。虽然RAG技术在搜索和推荐系统中具有潜在应用价值（如文档检索、信息增强），但论文标题未明确指向RecSys/Search/Ads领域的特定问题或应用场景，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 03:40:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03413v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03413v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As an effective method to boost the performance of Large Language Models (LLMs) on the question answering (QA) task, Retrieval-Augmented Generation (RAG), which queries highly relevant information from external complex documents, has attracted tremendous attention from both industry and academia. Existing RAG approaches often focus on general documents, and they overlook the fact that many real-world documents (such as books, booklets, handbooks, etc.) have a hierarchical structure, which organizes their content from different granularity levels, leading to poor performance for the QA task. To address these limitations, we introduce BookRAG, a novel RAG approach targeted for documents with a hierarchical structure, which exploits logical hierarchies and traces entity relations to query the highly relevant information. Specifically, we build a novel index structure, called BookIndex, by extracting a hierarchical tree from the document, which serves as the role of its table of contents, using a graph to capture the intricate relationships between entities, and mapping entities to tree nodes. Leveraging the BookIndex, we then propose an agent-based query method inspired by the Information Foraging Theory, which dynamically classifies queries and employs a tailored retrieval workflow. Extensive experiments on three widely adopted benchmarks demonstrate that BookRAG achieves state-of-the-art performance, significantly outperforming baselines in both retrieval recall and QA accuracy while maintaining competitive efficiency.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04072v1" target="_blank" rel="noopener noreferrer">
                SkillFactory：用于学习认知行为的自蒸馏方法
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            SkillFactory: Self-Distillation For Learning Cognitive Behaviors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zayne Sprague, Jack Lu, Manya Wadhwa, Sedrick Keh, Mengye Ren, Greg Durrett
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及自蒸馏技术，这属于模型压缩和知识迁移的范畴，可能对提升推荐系统或搜索模型的效率有一定帮助。然而，标题中提到的“学习认知行为”较为抽象，未明确指向推荐、搜索或广告领域的特定应用，且缺乏与Transformer架构、LLM技术或异构数据建模的直接关联，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:54:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04072v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04072v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reasoning models leveraging long chains of thought employ various cognitive skills, such as verification of their answers, backtracking, retrying by an alternate method, and more. Previous work has shown that when a base language model exhibits these skills, training that model further with reinforcement learning (RL) can learn to leverage them. How can we get models to leverage skills that aren't exhibited by base models? Our work, SkillFactory, is a method for fine-tuning models to roughly learn these skills during a supervised fine-tuning (SFT) stage prior to RL. Our approach does not rely on distillation from a stronger model, but instead uses samples from the model itself, rearranged to provide training data in the format of those skills. These "silver" SFT traces may be imperfect, but are nevertheless effective for priming a model to acquire skills during RL. Our evaluation shows that (1) starting from SkillFactory SFT initialization helps a model to generalize to harder variants of a task post-RL, despite lower performance pre-RL; (2) cognitive skills are indeed used by the model; (3) RLed SkillFactory models are more robust to regression on out-of-domain tasks than RLed base models. Our work suggests that inductive biases learned prior to RL help models learn robust cognitive skill use.
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            <a href="https://www.alphaxiv.org/abs/2512.03803v1" target="_blank" rel="noopener noreferrer">
                增强序列到序列模型的指令遵循能力：T5的DoLA适配方法
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            Enhancing Instruction-Following Capabilities in Seq2Seq Models: DoLA Adaptations for T5
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huey Sun, Anabel Yong, Lorenzo Gilly, Felipe Jin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注提升T5模型的指令遵循能力，这属于LLM核心技术的进步，可能应用于搜索和推荐系统中更精确的查询理解和响应生成。然而，论文标题未明确说明与推荐系统、搜索或广告的具体应用连接，因此相关性有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 13:54:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03803v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03803v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Contrastive decoding is a lightweight and effective inference-time method that improves the quality of text generation in Large Language Models. However, algorithms such as DoLa (Decoding by Contrastive Layers) have only been implemented in decoder-only architectures and studied for their impact on improving factuality. This work adapts DoLa for the T5 and FLAN-T5 model families and evaluates its impact on the models' instruction following capabilities, which to our knowledge is the first implementation of a contrastive decoding strategy in an encoder-decoder architecture. Our results show that DoLa improves the faithfulness of text generation for certain categories of tasks and harms others. To understand these results, we present a layer-by-layer analysis of logit evolution in a FLAN-T5 model to quantify DoLa's impact on token output probabilities.
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            <a href="https://www.alphaxiv.org/abs/2512.03794v1" target="_blank" rel="noopener noreferrer">
                AdaptVision：通过自适应视觉采集实现高效视觉语言模型
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zichuan Lin, Yicheng Liu, Yang Yang, Lvfang Tao, Deheng Ye
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型（VLM）的效率优化，属于VLM技术范畴。虽然标题中提到了“自适应视觉采集”这一效率改进技术，但论文没有明确展示其在推荐系统、搜索或广告领域的直接应用潜力。该工作可能更偏向于纯粹的视觉语言模型优化，而非针对异构数据建模的VLM类比应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 13:43:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03794v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03794v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision-Language Models (VLMs) have achieved remarkable success in visual question answering tasks, but their reliance on large numbers of visual tokens introduces significant computational overhead. While existing efficient VLM approaches reduce visual tokens through fixed-ratio compression, they operate passively and lack the ability to adapt to varying task requirements. This motivates a fundamental question: Can VLMs autonomously determine the minimum number of visual tokens required for each sample? Inspired by human active vision mechanisms, we introduce AdaptVision, an efficient VLM paradigm that enables adaptive visual token acquisition through a coarse-to-fine approach. Our model initially processes compressed visual tokens from low-resolution images and selectively acquires additional visual information by invoking a bounding box tool to crop key regions when necessary. We train AdaptVision using a reinforcement learning framework that carefully balances accuracy and efficiency. Central to our approach is Decoupled Turn Policy Optimization (DTPO), which decouples the learning objective into two components: (1) tool learning, which optimizes correct tool utilization, and (2) accuracy improvement, which refines the generated responses to improve answer correctness. Based on this formulation, we further decouple advantage estimation by computing separate advantages for tokens associated with each objective. This formulation enables more effective optimization for AdaptVision compared to vanilla GRPO. Comprehensive experiments across multiple VQA benchmarks demonstrate that AdaptVision achieves superior performance while consuming substantially fewer visual tokens than state-of-the-art efficient VLM methods.
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            <a href="https://www.alphaxiv.org/abs/2512.03759v1" target="_blank" rel="noopener noreferrer">
                基于序列级视角的扩散大语言模型原则性强化学习
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingyang Ou, Jiaqi Han, Minkai Xu, Shaoxuan Xu, Jianwen Xie, Stefano Ermon, Yi W...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及扩散模型和强化学习在LLMs中的应用，属于'Enabling LLM Tech'范畴，可能通过改进序列生成质量间接应用于推荐或搜索的序列建模。然而，标题未明确提及RecSys/Search/Ads应用，且强化学习（RL）在无明确领域相关性时属于限制主题，因此相关性较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 13:05:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03759v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03759v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reinforcement Learning (RL) has proven highly effective for autoregressive language models, but adapting these methods to diffusion large language models (dLLMs) presents fundamental challenges. The core difficulty lies in likelihood approximation: while autoregressive models naturally provide token-level conditional probabilities essential for token-level RL objectives (e.g., GRPO), dLLMs generate sequences through iterative non-autoregressive denoising steps that lack this factorization. To address this fundamental mismatch, we propose ELBO-based Sequence-level Policy Optimization (ESPO), a principled RL framework that treats entire sequence generation as a single action and uses the ELBO as a tractable sequence-level likelihood proxy. Our method incorporates per-token normalization of importance ratios and robust KL-divergence estimation to ensure stable large-scale training. Extensive experiments on mathematical reasoning, coding, and planning tasks demonstrate that ESPO significantly outperforms token-level baselines, achieving dramatic improvements of 20-40 points on the Countdown task, while maintaining consistent gains on math and coding benchmarks. Our approach establishes sequence-level optimization as a principled and empirically effective paradigm for RL in dLLMs. Our code is available at https://github.com/ML-GSAI/ESPO.
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            <a href="https://www.alphaxiv.org/abs/2512.03463v1" target="_blank" rel="noopener noreferrer">
                文本打印图像：弥合图像-文本模态鸿沟以实现以文本为中心的大型视觉语言模型训练
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        <div class="mb-2 text-base text-gray-700">
            Text-Printed Image: Bridging the Image-Text Modality Gap for Text-centric Training of Large Vision-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shojiro Yamabe, Futa Waseda, Daiki Shiono, Tsubasa Takahashi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及视觉语言模型（VLM）和模态对齐，但其核心关注的是纯视觉语言模型的训练方法改进，而非将VLM思想应用于推荐/搜索/广告中的异构数据处理。虽然模态对齐思想在理论上可类比于推荐系统中用户序列与上下文特征的统一建模，但论文本身缺乏明确的推荐/搜索/广告应用导向，因此相关性有限。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:36:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03463v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03463v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent large vision-language models (LVLMs) have been applied to diverse VQA tasks. However, achieving practical performance typically requires task-specific fine-tuning with large numbers of image-text pairs, which are costly to collect. In this work, we study text-centric training, a setting where only textual descriptions are available and no real images are provided, as a paradigm for low-cost data scaling. Unlike images, whose collection is often restricted by privacy constraints and scarcity in niche domains, text is widely available. Moreover, text is easily editable, enabling automatic diversification and expansion with LLMs at minimal human effort. While this offers clear advantages over image collection in terms of scalability and cost, training on raw text without images still yields limited gains on VQA tasks because of the image-text modality gap. To address this issue, we propose a Text-Printed Image (TPI), which generates synthetic images by directly rendering the given textual description on a plain white canvas. This simple rendering projects text into the image modality and can be integrated into arbitrary existing LVLM training pipelines at low cost. Moreover, TPI preserves the semantics of the text, whereas text-to-image models often fail to do. Across four models and seven benchmarks, our systematic experiments show that TPI enables more effective text-centric training than synthetic images generated by a diffusion model. We further explore TPI as a low-cost data-augmentation strategy and demonstrate its practical utility. Overall, our findings highlight the significant potential of text-centric training and, more broadly, chart a path toward fully automated data generation for LVLMs.
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            <a href="https://www.alphaxiv.org/abs/2512.03360v1" target="_blank" rel="noopener noreferrer">
                从假设到前提：基于大语言模型的选择性符号翻译逆向逻辑推理
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qingchuan Li, Mingyue Cheng, Zirui Liu, Daoyu Wang, Yuting Zeng, Tongxuan Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及LLM的逻辑推理能力，属于核心LLM技术进展范畴。虽然逻辑推理在搜索（如查询理解、相关性判断）和推荐系统（如可解释性、规则生成）中有潜在应用，但论文标题未明确指向这些领域的具体应用，且可能更偏向纯NLP推理研究。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 01:52:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03360v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03360v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Logical reasoning is a core challenge in natural language understanding and a fundamental capability of artificial intelligence, underpinning scientific discovery, mathematical theorem proving, and complex decision-making. Despite the remarkable progress of large language models (LLMs), most current approaches still rely on forward reasoning paradigms, generating step-by-step rationales from premises to conclusions. However, such methods often suffer from redundant inference paths, hallucinated steps, and semantic drift, resulting in inefficient and unreliable reasoning. In this paper, we propose a novel framework, Hypothesis-driven Backward Logical Reasoning (HBLR). The core idea is to integrate confidence-aware symbolic translation with hypothesis-driven backward reasoning. In the translation phase, only high-confidence spans are converted into logical form, such as First-Order Logic (FOL), while uncertain content remains in natural language. A translation reflection module further ensures semantic fidelity by evaluating symbolic outputs and reverting lossy ones back to text when necessary. In the reasoning phase, HBLR simulates human deductive thinking by assuming the conclusion is true and recursively verifying its premises. A reasoning reflection module further identifies and corrects flawed inference steps, enhancing logical coherence. Extensive experiments on five reasoning benchmarks demonstrate that HBLR consistently outperforms strong baselines in both accuracy and efficiency.
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            <a href="https://www.alphaxiv.org/abs/2512.04084v1" target="_blank" rel="noopener noreferrer">
                SimFlow：简化的端到端潜在归一化流训练
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            SimFlow: Simplified and End-to-End Training of Latent Normalizing Flows
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qinyu Zhao, Guangting Zheng, Tao Yang, Rui Zhu, Xingjian Leng, Stephen Gould, Li...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及归一化流（Normalizing Flows），这是一种生成模型技术，属于核心LLM技术中的概率建模方法。虽然归一化流在密度估计和生成建模中有应用，但论文标题未明确表明其在推荐系统、搜索或广告中的直接应用潜力，且未提及Transformer架构或异构数据处理等具体相关方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04084v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04084v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Normalizing Flows (NFs) learn invertible mappings between the data and a Gaussian distribution. Prior works usually suffer from two limitations. First, they add random noise to training samples or VAE latents as data augmentation, introducing complex pipelines including extra noising and denoising steps. Second, they use a pretrained and frozen VAE encoder, resulting in suboptimal reconstruction and generation quality. In this paper, we find that the two issues can be solved in a very simple way: just fixing the variance (which would otherwise be predicted by the VAE encoder) to a constant (e.g., 0.5). On the one hand, this method allows the encoder to output a broader distribution of tokens and the decoder to learn to reconstruct clean images from the augmented token distribution, avoiding additional noise or denoising design. On the other hand, fixed variance simplifies the VAE evidence lower bound, making it stable to train an NF with a VAE jointly. On the ImageNet $256 \times 256$ generation task, our model SimFlow obtains a gFID score of 2.15, outperforming the state-of-the-art method STARFlow (gFID 2.40). Moreover, SimFlow can be seamlessly integrated with the end-to-end representation alignment (REPA-E) method and achieves an improved gFID of 1.91, setting a new state of the art among NFs.
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            <a href="https://www.alphaxiv.org/abs/2512.03673v1" target="_blank" rel="noopener noreferrer">
                ConvRot：基于旋转的即插即用4位量化扩散变换器
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            ConvRot: Rotation-Based Plug-and-Play 4-bit Quantization for Diffusion Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Feice Huang, Zuliang Han, Xing Zhou, Yihuang Chen, Lifei Zhu, Haoqian Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散模型中的Transformer量化技术，属于Transformer架构效率优化范畴。虽然量化技术可能间接应用于推荐/搜索系统中的模型压缩，但论文明确聚焦于扩散模型而非推荐/搜索/广告领域的核心应用场景，因此相关性有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:02:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03673v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03673v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diffusion transformers have demonstrated strong capabilities in generating high-quality images. However, as model size increases, the growing memory footprint and inference latency pose significant challenges for practical deployment. Recent studies in large language models (LLMs) show that rotation-based techniques can smooth outliers and enable 4-bit quantization, but these approaches often incur substantial overhead and struggle with row-wise outliers in diffusion transformers. To address these challenges, we propose ConvRot, a group-wise rotation-based quantization method that leverages regular Hadamard transform (RHT) to suppress both row-wise and column-wise outliers while reducing complexity from quadratic to linear. Building on this, we design ConvLinear4bit, a plug-and-play module that integrates rotation, quantization, GEMM, and dequantization, enabling W4A4 inference without retraining and preserving visual quality. Experiments on FLUX.1-dev demonstrate a 2.26$\times$ speedup and 4.05$\times$ memory reduction while maintaining image fidelity. To our knowledge, this is the first application of rotation-based quantization for plug-and-play W4A4 inference in diffusion transformers.
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            <a href="https://www.alphaxiv.org/abs/2512.03807v1" target="_blank" rel="noopener noreferrer">
                基于整数规划和启发式算法的布尔矩阵分解算法
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            <i class="fa fa-star mr-1"></i>2/10
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            Algorithms for Boolean Matrix Factorization using Integer Programming and Heuristics
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Christos Kolomvakis, Thomas Bobille, Arnaud Vandaele, Nicolas Gillis
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及布尔矩阵分解算法，属于传统机器学习方法，与LLMs、推荐系统、搜索或广告的核心进展没有直接关联。虽然矩阵分解在推荐系统中有历史应用，但该论文专注于布尔矩阵的特定分解方法，没有展示与当前LLM技术、Transformer架构或异构数据统一建模的明显联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 13:55:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03807v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03807v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">eess.SP</span><span class="category-tag">math.OC</span><span class="category-tag">stat.ML</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Boolean matrix factorization (BMF) approximates a given binary input matrix as the product of two smaller binary factors. Unlike binary matrix factorization based on standard arithmetic, BMF employs the Boolean OR and AND operations for the matrix product, which improves interpretability and reduces the approximation error. It is also used in role mining and computer vision. In this paper, we first propose algorithms for BMF that perform alternating optimization (AO) of the factor matrices, where each subproblem is solved via integer programming (IP). We then design different approaches to further enhance AO-based algorithms by selecting an optimal subset of rank-one factors from multiple runs. To address the scalability limits of IP-based methods, we introduce new greedy and local-search heuristics. We also construct a new C++ data structure for Boolean vectors and matrices that is significantly faster than existing ones and is of independent interest, allowing our heuristics to scale to large datasets. We illustrate the performance of all our proposed methods and compare them with the state of the art on various real datasets, both with and without missing data, including applications in topic modeling and imaging.
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            <a href="https://www.alphaxiv.org/abs/2512.03737v1" target="_blank" rel="noopener noreferrer">
                AR-Med：基于大语言模型驱动信息增强的医疗搜索自动化相关性提升
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            AR-Med: Automated Relevance Enhancement in Medical Search via LLM-Driven Information Augmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chuyue Wang, Jie Feng, Yuxi Wu, Hang Zhang, Zhiguo Fan, Bing Cheng, Wei Lin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医疗领域搜索应用（Medical Search），属于您列出的不相关主题中的“Medical, Biology, Chemistry, Physics or other domain-specific applications”。虽然涉及LLM在搜索中的应用，但其领域特异性使其与您关注的通用推荐系统、搜索或广告核心进展相关性极低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 12:34:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03737v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03737v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate and reliable search on online healthcare platforms is critical for user safety and service efficacy. Traditional methods, however, often fail to comprehend complex and nuanced user queries, limiting their effectiveness. Large language models (LLMs) present a promising solution, offering powerful semantic understanding to bridge this gap. Despite their potential, deploying LLMs in this high-stakes domain is fraught with challenges, including factual hallucinations, specialized knowledge gaps, and high operational costs. To overcome these barriers, we introduce \textbf{AR-Med}, a novel framework for \textbf{A}utomated \textbf{R}elevance assessment for \textbf{Med}ical search that has been successfully deployed at scale on the Online Medical Delivery Platforms. AR-Med grounds LLM reasoning in verified medical knowledge through a retrieval-augmented approach, ensuring high accuracy and reliability. To enable efficient online service, we design a practical knowledge distillation scheme that compresses large teacher models into compact yet powerful student models. We also introduce LocalQSMed, a multi-expert annotated benchmark developed to guide model iteration and ensure strong alignment between offline and online performance. Extensive experiments show AR-Med achieves an offline accuracy of over 93\%, a 24\% absolute improvement over the original online system, and delivers significant gains in online relevance and user satisfaction. Our work presents a practical and scalable blueprint for developing trustworthy, LLM-powered systems in real-world healthcare applications.
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            <a href="https://www.alphaxiv.org/abs/2512.04032v1" target="_blank" rel="noopener noreferrer">
                Jina-VLM：小型多语言视觉语言模型
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        <div class="mb-2 text-base text-gray-700">
            Jina-VLM: Small Multilingual Vision Language Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Andreas Koukounas, Georgios Mastrapas, Florian Hönicke, Sedigheh Eslami, Guillau...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文属于视觉语言模型（VLM）领域，虽然标题中提到了多语言能力，但核心是视觉-语言跨模态建模。根据您的关注点，VLM类比于异构数据处理（如将上下文特征和用户序列视为不同模态进行统一建模）可能有一定启发，但该论文标题未明确表明其在推荐系统、搜索或广告中的具体应用潜力，且可能更偏向纯视觉或多语言NLP方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:13:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04032v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04032v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present Jina-VLM, a 2.4B parameter vision-language model that achieves state-of-the-art multilingual visual question answering among open 2B-scale VLMs. The model couples a SigLIP2 vision encoder with a Qwen3 language backbone through an attention-pooling connector that enables token-efficient processing of arbitrary-resolution images. Across standard VQA benchmarks and multilingual evaluations, Jina-VLM outperforms comparable models while preserving competitive text-only performance.
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            <a href="https://www.alphaxiv.org/abs/2512.03976v1" target="_blank" rel="noopener noreferrer">
                面向低资源藏语的大语言模型适配：一项两阶段持续监督微调研究
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            Adapting Large Language Models to Low-Resource Tibetan: A Two-Stage Continual and Supervised Fine-Tuning Study
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lifeng Chen, Ryan Lai, Tianming Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注特定语言（藏语）的LLM适配技术，属于语言特定的工程优化，而非核心LLM技术进展或架构创新。虽然涉及LLM微调，但缺乏对推荐系统、搜索或广告领域的明确应用潜力或技术通用性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:06:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03976v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03976v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Adapting large language models (LLMs) to low-resource languages remains a major challenge due to data scarcity and cross-lingual drift. This work presents a two-stage adaptation of Qwen2.5-3B to Tibetan, a morphologically rich and underrepresented language. We employ Continual Pretraining (CPT) to establish Tibetan linguistic grounding, followed by Supervised Fine-Tuning (SFT) for task and translation specialization. Empirical evaluations demonstrate a consistent decrease in perplexity (from 2.98 $\rightarrow$ 1.54) and substantial improvements in Chinese$\rightarrow$Tibetan translation quality (BLEU: 0.046 $\rightarrow$ 0.261; chrF: 2.2 $\rightarrow$ 6.6). Layer-wise analysis across 435 layers in Qwen3-4B reveals that adaptation primarily concentrates on embedding and output heads, with mid--late MLP projections encoding domain-specific transformations. Our findings suggest that CPT constructs a Tibetan semantic manifold while SFT sharpens task alignment with minimal representational disruption. This study provides the first quantitative exploration of Tibetan adaptation dynamics for LLMs, and offers an open, reproducible framework for extending multilingual foundation models to low-resource settings.
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                上下文表示劫持
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            In-Context Representation Hijacking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Itay Yona, Amir Sarid, Michael Karasik, Yossi Gandelsman
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题暗示可能涉及LLM安全或对抗性攻击领域，这属于隐私/安全范畴，属于明确的无关主题。虽然可能涉及表示学习，但缺乏与推荐系统、搜索或广告的直接联系，且未表明有明确的实际应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 13:19:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03771v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03771v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CR</span><span class="category-tag">cs.LG</span></div>
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                    We introduce \textbf{Doublespeak}, a simple \emph{in-context representation hijacking} attack against large language models (LLMs). The attack works by systematically replacing a harmful keyword (e.g., \textit{bomb}) with a benign token (e.g., \textit{carrot}) across multiple in-context examples, provided a prefix to a harmful request. We demonstrate that this substitution leads to the internal representation of the benign token converging toward that of the harmful one, effectively embedding the harmful semantics under a euphemism. As a result, superficially innocuous prompts (e.g., ``How to build a carrot?'') are internally interpreted as disallowed instructions (e.g., ``How to build a bomb?''), thereby bypassing the model's safety alignment. We use interpretability tools to show that this semantic overwrite emerges layer by layer, with benign meanings in early layers converging into harmful semantics in later ones. Doublespeak is optimization-free, broadly transferable across model families, and achieves strong success rates on closed-source and open-source systems, reaching 74\% ASR on Llama-3.3-70B-Instruct with a single-sentence context override. Our findings highlight a new attack surface in the latent space of LLMs, revealing that current alignment strategies are insufficient and should instead operate at the representation level.
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                DZ-TDPO：面向长上下文对话中可变状态追踪的非破坏性时序对齐方法
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            DZ-TDPO: Non-Destructive Temporal Alignment for Mutable State Tracking in Long-Context Dialogue
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yijun Liao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注长上下文对话中的状态追踪和时序对齐问题，属于对话系统和NLP领域。虽然涉及长上下文处理技术，但缺乏明确的与推荐系统、搜索或广告领域的应用连接。论文焦点更偏向对话状态管理而非推荐/搜索/广告中的核心问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:56:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03704v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03704v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Long-context dialogue systems suffer from State Inertia, where static constraints prevent models from resolving conflicts between evolving user intents and established historical context. To address this, we propose DZ-TDPO, a non-destructive alignment framework that synergizes conflict-aware dynamic KL constraints with a learnable temporal attention bias. Experiments on the Multi-Session Chat (MSC) dataset demonstrate that DZ-TDPO achieves state-of-the-art win rates (86.2% on Phi-3.5) while maintaining robust zero-shot generalization. Crucially, our scaling analysis reveals a "Capacity-Stability Trade-off": while smaller models incur an "alignment tax" (perplexity surge) to overcome historical inertia, the larger Qwen2.5-7B model achieves near-perfect alignment (99.4% win rate) with negligible perplexity overhead. This confirms that TAI can be alleviated via precise attention regulation rather than destructive weight updates, preserving general capabilities (MMLU) across model scales. Code and data are available: https://github.com/lyj20071013/DZ-TDPO
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            <a href="https://www.alphaxiv.org/abs/2512.03676v1" target="_blank" rel="noopener noreferrer">
                不同类型的句法一致性在大语言模型中招募相同的单元
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            Different types of syntactic agreement recruit the same units within large language models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daria Kryvosheieva, Andrea de Varda, Evelina Fedorenko, Greta Tuckute
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究大语言模型内部句法处理机制，属于LLM内部工作机制分析。虽然涉及大语言模型技术，但主要关注语言学层面的句法处理，与推荐系统、搜索或广告的应用场景关联较弱，对当前关注领域的直接价值有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:07:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03676v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03676v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the models remains an open question. We investigate whether different syntactic phenomena recruit shared or distinct components in LLMs. Using a functional localization approach inspired by cognitive neuroscience, we identify the LLM units most responsive to 67 English syntactic phenomena in seven open-weight models. These units are consistently recruited across sentences containing the phenomena and causally support the models' syntactic performance. Critically, different types of syntactic agreement (e.g., subject-verb, anaphor, determiner-noun) recruit overlapping sets of units, suggesting that agreement constitutes a meaningful functional category for LLMs. This pattern holds in English, Russian, and Chinese; and further, in a cross-lingual analysis of 57 diverse languages, structurally more similar languages share more units for subject-verb agreement. Taken together, these findings reveal that syntactic agreement-a critical marker of syntactic dependencies-constitutes a meaningful category within LLMs' representational spaces.
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            <a href="https://www.alphaxiv.org/abs/2512.03582v1" target="_blank" rel="noopener noreferrer">
                偏见新闻文章中的细粒度叙事分类
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            Fine-grained Narrative Classification in Biased News Articles
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zeba Afroz, Harsh Vardhan, Pawan Bhakuni, Aanchal Punia, Rajdeep Kumar, Md. Shad...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注新闻文章中的偏见检测和叙事分类，这属于内容理解和文本分析领域。虽然内容理解是搜索和推荐系统的组成部分，但该论文的焦点是新闻领域的偏见分析，而非直接针对推荐系统、搜索或广告中的核心排序、检索或用户建模问题。它可能对内容质量评估有间接价值，但与您关注的LLM技术、Transformer架构进展、直接LLM应用或异构数据统一建模等核心方向关联较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:07:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03582v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03582v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Narratives are the cognitive and emotional scaffolds of propaganda. They organize isolated persuasive techniques into coherent stories that justify actions, attribute blame, and evoke identification with ideological camps. In this paper, we propose a novel fine-grained narrative classification in biased news articles. We also explore article-bias classification as the precursor task to narrative classification and fine-grained persuasive technique identification. We develop INDI-PROP, the first ideologically grounded fine-grained narrative dataset with multi-level annotation for analyzing propaganda in Indian news media. Our dataset INDI-PROP comprises 1,266 articles focusing on two polarizing socio-political events in recent times: CAA and the Farmers' protest. Each article is annotated at three hierarchical levels: (i) ideological article-bias (pro-government, pro-opposition, neutral), (ii) event-specific fine-grained narrative frames anchored in ideological polarity and communicative intent, and (iii) persuasive techniques. We propose FANTA and TPTC, two GPT-4o-mini guided multi-hop prompt-based reasoning frameworks for the bias, narrative, and persuasive technique classification. FANTA leverages multi-layered communicative phenomena by integrating information extraction and contextual framing for hierarchical reasoning. On the other hand, TPTC adopts systematic decomposition of persuasive cues via a two-stage approach. Our evaluation suggests substantial improvement over underlying baselines in each case.
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            <a href="https://www.alphaxiv.org/abs/2512.03503v1" target="_blank" rel="noopener noreferrer">
                理解大语言模型在抽象摘要任务中的推理机制
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            Understanding LLM Reasoning for Abstractive Summarization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haohan Yuan, Siu Cheung Hui, Haopeng Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于LLM在抽象摘要任务中的推理机制研究，这属于纯粹的NLP中心话题，与您关注的推荐系统、搜索或广告领域没有直接关联。虽然涉及LLM技术，但缺乏明确的跨领域应用潜力，特别是没有展示如何将这种推理机制应用于异构数据处理或推荐系统等核心领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 06:52:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03503v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03503v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    While the reasoning capabilities of Large Language Models (LLMs) excel in analytical tasks such as mathematics and code generation, their utility for abstractive summarization remains widely assumed but largely unverified. To bridge this gap, we first tailor general reasoning strategies to the summarization domain. We then conduct a systematic, large scale comparative study of 8 reasoning strategies and 3 Large Reasoning Models (LRMs) across 8 diverse datasets, assessing both summary quality and faithfulness. Our findings show that reasoning is not a universal solution and its effectiveness is highly dependent on the specific strategy and context. Specifically, we observe a trade-off between summary quality and factual faithfulness: explicit reasoning strategies tend to improve fluency at the expense of factual grounding, while implicit reasoning in LRMs exhibits the inverse pattern. Furthermore, increasing an LRM's internal reasoning budget does not improve, and can even hurt, factual consistency, suggesting that effective summarization demands faithful compression rather than creative over-thinking.
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            <a href="https://www.alphaxiv.org/abs/2512.03499v1" target="_blank" rel="noopener noreferrer">
                NAS-LoRA：通过可搜索适配赋能视觉基础模型的高效参数微调
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            NAS-LoRA: Empowering Parameter-Efficient Fine-Tuning for Visual Foundation Models with Searchable Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Renqi Chen, Haoyang Su, Shixiang Tang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉基础模型的参数高效微调技术，属于计算机视觉领域。虽然提到了NAS（神经架构搜索）和LoRA（低秩适配）等通用技术，但论文标题明确限定于视觉模型，没有表明在推荐系统、搜索或广告领域的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 06:47:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03499v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03499v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    The Segment Anything Model (SAM) has emerged as a powerful visual foundation model for image segmentation. However, adapting SAM to specific downstream tasks, such as medical and agricultural imaging, remains a significant challenge. To address this, Low-Rank Adaptation (LoRA) and its variants have been widely employed to enhancing SAM's adaptation performance on diverse domains. Despite advancements, a critical question arises: can we integrate inductive bias into the model? This is particularly relevant since the Transformer encoder in SAM inherently lacks spatial priors within image patches, potentially hindering the acquisition of high-level semantic information. In this paper, we propose NAS-LoRA, a new Parameter-Efficient Fine-Tuning (PEFT) method designed to bridge the semantic gap between pre-trained SAM and specialized domains. Specifically, NAS-LoRA incorporates a lightweight Neural Architecture Search (NAS) block between the encoder and decoder components of LoRA to dynamically optimize the prior knowledge integrated into weight updates. Furthermore, we propose a stage-wise optimization strategy to help the ViT encoder balance weight updates and architectural adjustments, facilitating the gradual learning of high-level semantic information. Various Experiments demonstrate our NAS-LoRA improves existing PEFT methods, while reducing training cost by 24.14% without increasing inference cost, highlighting the potential of NAS in enhancing PEFT for visual foundation models.
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            <a href="https://www.alphaxiv.org/abs/2512.03442v1" target="_blank" rel="noopener noreferrer">
                PretrainZero：基于强化学习的主动预训练
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            PretrainZero: Reinforcement Active Pretraining
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xingrun Xing, Zhiyuan Fan, Jie Lou, Guoqi Li, Jiajun Zhang, Debing Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及强化学习与预训练的结合，但未明确说明与推荐系统、搜索或广告的直接关联。虽然主动学习可能优化数据选择，但强化学习在标题中占主导，而您的关注点明确排除无明确相关性的强化学习论文。需要更多上下文判断其是否适用于您的领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 04:51:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03442v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03442v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Mimicking human behavior to actively learning from general experience and achieve artificial general intelligence has always been a human dream. Recent reinforcement learning (RL) based large-thinking models demonstrate impressive expert-level abilities, i.e., software and math, but still rely heavily on verifiable rewards in specific domains, placing a significant bottleneck to extend the performance boundary of general reasoning capabilities. In this work, we propose PretrainZero, a reinforcement active learning framework built on the pretraining corpus to extend RL from domain-specific post-training to general pretraining. PretrainZero features the following characteristics: 1) Active pretraining: inspired by the active learning ability of humans, PretrainZero learns a unified reasoning policy to actively identify reasonable and informative contents from pretraining corpus, and reason to predict these contents by RL. 2) Self-supervised learning: without any verifiable labels, pretrained reward models, or supervised fine-tuning, we directly pretrain reasoners from 3 to 30B base models on the general Wikipedia corpus using RL, significantly breaking the verification data-wall for general reasoning. 3) Verification scaling: by tackling increasingly challenging masked spans, PretrainZero substantially enhances the general reasoning abilities of pretrained base models. In reinforcement pretraining, PretrainZero improves Qwen3-4B-Base for 8.43, 5.96 and 10.60 on MMLU-Pro, SuperGPQA and math average benchmarks. In post-training, the pretrained models can also serve as reasoning foundation models for downstream RLVR tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.03381v1" target="_blank" rel="noopener noreferrer">
                协作情境游戏中语言使用特征分析
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            Characterizing Language Use in a Collaborative Situated Game
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nicholas Tomlin, Naitian Zhou, Eve Fleisig, Liangyuan, Chen, Téa Wright, Lauren ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于协作游戏中的语言使用分析，属于特定领域的行为研究。虽然涉及语言分析，但缺乏与推荐系统、搜索或广告领域的明确联系，也没有涉及LLM技术、Transformer架构进展或异构数据统一建模等当前关注的核心技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 02:29:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03381v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03381v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Cooperative video games, where multiple participants must coordinate by communicating and reasoning under uncertainty in complex environments, yield a rich source of language data. We collect the Portal Dialogue Corpus: a corpus of 11.5 hours of spoken human dialogue in the co-op mode of the popular Portal 2 virtual puzzle game, comprising 24.5K total utterances. We analyze player language and behavior, identifying a number of linguistic phenomena that rarely appear in most existing chitchat or task-oriented dialogue corpora, including complex spatial reference, clarification and repair, and ad-hoc convention formation. To support future analyses of language use in complex, situated, collaborative problem-solving scenarios, we publicly release the corpus, which comprises player videos, audio, transcripts, game state data, and both manual and automatic annotations of language data.
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            <a href="https://www.alphaxiv.org/abs/2512.04040v1" target="_blank" rel="noopener noreferrer">
                RELIC：具备长时记忆的交互式视频世界模型
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            RELIC: Interactive Video World Model with Long-Horizon Memory
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yicong Hong, Yiqun Mei, Chongjian Ge, Yiran Xu, Yang Zhou, Sai Bi, Yannick Hold-...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频世界模型和长时记忆，属于计算机视觉和序列建模领域。虽然世界模型和记忆机制在理论上可能对推荐系统中的用户行为序列建模有启发，但论文标题明确聚焦于视频交互，缺乏与推荐/搜索/广告领域的直接关联或明确的应用潜力说明。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:29:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04040v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04040v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    A truly interactive world model requires three key ingredients: real-time long-horizon streaming, consistent spatial memory, and precise user control. However, most existing approaches address only one of these aspects in isolation, as achieving all three simultaneously is highly challenging-for example, long-term memory mechanisms often degrade real-time performance. In this work, we present RELIC, a unified framework that tackles these three challenges altogether. Given a single image and a text description, RELIC enables memory-aware, long-duration exploration of arbitrary scenes in real time. Built upon recent autoregressive video-diffusion distillation techniques, our model represents long-horizon memory using highly compressed historical latent tokens encoded with both relative actions and absolute camera poses within the KV cache. This compact, camera-aware memory structure supports implicit 3D-consistent content retrieval and enforces long-term coherence with minimal computational overhead. In parallel, we fine-tune a bidirectional teacher video model to generate sequences beyond its original 5-second training horizon, and transform it into a causal student generator using a new memory-efficient self-forcing paradigm that enables full-context distillation over long-duration teacher as well as long student self-rollouts. Implemented as a 14B-parameter model and trained on a curated Unreal Engine-rendered dataset, RELIC achieves real-time generation at 16 FPS while demonstrating more accurate action following, more stable long-horizon streaming, and more robust spatial-memory retrieval compared with prior work. These capabilities establish RELIC as a strong foundation for the next generation of interactive world modeling.
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            <a href="https://www.alphaxiv.org/abs/2512.04039v1" target="_blank" rel="noopener noreferrer">
                快速高效的正规化流及其在图像生成模型中的应用
            </a>
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            Fast & Efficient Normalizing Flows and Applications of Image Generative Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sandeep Nagar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注正规化流和图像生成模型，属于纯粹的生成式AI领域，与推荐系统、搜索或广告的核心排名任务没有直接关联。虽然正规化流在理论上可以用于密度估计，但论文标题明确指向图像生成应用，这超出了当前关注的异构数据建模或直接LLM应用范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:29:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04039v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04039v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This thesis presents novel contributions in two primary areas: advancing the efficiency of generative models, particularly normalizing flows, and applying generative models to solve real-world computer vision challenges. The first part introduce significant improvements to normalizing flow architectures through six key innovations: 1) Development of invertible 3x3 Convolution layers with mathematically proven necessary and sufficient conditions for invertibility, (2) introduction of a more efficient Quad-coupling layer, 3) Design of a fast and efficient parallel inversion algorithm for kxk convolutional layers, 4) Fast & efficient backpropagation algorithm for inverse of convolution, 5) Using inverse of convolution, in Inverse-Flow, for the forward pass and training it using proposed backpropagation algorithm, and 6) Affine-StableSR, a compact and efficient super-resolution model that leverages pre-trained weights and Normalizing Flow layers to reduce parameter count while maintaining performance. The second part: 1) An automated quality assessment system for agricultural produce using Conditional GANs to address class imbalance, data scarcity and annotation challenges, achieving good accuracy in seed purity testing; 2) An unsupervised geological mapping framework utilizing stacked autoencoders for dimensionality reduction, showing improved feature extraction compared to conventional methods; 3) We proposed a privacy preserving method for autonomous driving datasets using on face detection and image inpainting; 4) Utilizing Stable Diffusion based image inpainting for replacing the detected face and license plate to advancing privacy-preserving techniques and ethical considerations in the field.; and 5) An adapted diffusion model for art restoration that effectively handles multiple types of degradation through unified fine-tuning.
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            <a href="https://www.alphaxiv.org/abs/2512.04012v1" target="_blank" rel="noopener noreferrer">
                视觉几何基础Transformer中的涌现性离群视图剔除
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Emergent Outlier View Rejection in Visual Geometry Grounded Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jisang Han, Sunghwan Hong, Jaewoo Jung, Wooseok Jang, Honggyu An, Qianqian Wang,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视觉几何和Transformer架构，属于计算机视觉领域。虽然涉及Transformer技术，但其核心是视觉几何的离群视图处理，与推荐系统、搜索或广告中的异构数据处理没有直接关联。标题中未暗示任何在推荐、搜索或广告领域的潜在应用，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:48:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04012v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04012v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Reliable 3D reconstruction from in-the-wild image collections is often hindered by "noisy" images-irrelevant inputs with little or no view overlap with others. While traditional Structure-from-Motion pipelines handle such cases through geometric verification and outlier rejection, feed-forward 3D reconstruction models lack these explicit mechanisms, leading to degraded performance under in-the-wild conditions. In this paper, we discover that the existing feed-forward reconstruction model, e.g., VGGT, despite lacking explicit outlier-rejection mechanisms or noise-aware training, can inherently distinguish distractor images. Through an in-depth analysis under varying proportions of synthetic distractors, we identify a specific layer that naturally exhibits outlier-suppressing behavior. Further probing reveals that this layer encodes discriminative internal representations that enable an effective noise-filtering capability, which we simply leverage to perform outlier-view rejection in feed-forward 3D reconstruction without any additional fine-tuning or supervision. Extensive experiments on both controlled and in-the-wild datasets demonstrate that this implicit filtering mechanism is consistent and generalizes well across diverse scenarios.
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            <a href="https://www.alphaxiv.org/abs/2512.04000v1" target="_blank" rel="noopener noreferrer">
                分而治之，再行关联：针对查询类型适配帧选择策略以实现长视频理解
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            Divide, then Ground: Adapting Frame Selection to Query Types for Long-Form Video Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jialuo Li, Bin Li, Jiahao Li, Yan Lu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频理解中的帧选择技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然视频理解技术可能间接应用于内容推荐场景，但论文标题未表明其与异构数据处理、Transformer架构改进或LLM技术应用等当前关注领域有明确联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:36:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04000v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04000v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    The application of Large Multimodal Models (LMMs) to long-form video understanding is constrained by limited context lengths and the computationally prohibitive cost of processing dense video tokens. Consequently, recent research has focused on query-aware frame selection, methods that often incur significant computational overhead. This paper challenges the assumption that such complex search mechanisms are universally necessary. We first identify and validate a query typology distinguishing between global query and localized query. We demonstrate that while uniform sampling is both effective and efficient for global queries, localized queries indeed necessitate query-aware selection for optimal performance. Building on this insight, we propose DIG, a training-free frame selection framework that adapts its strategy based on the query type. Specifically,DIG employs efficient uniform sampling for global queries while activating a specialized pipeline to extract query-relevant frames for localized queries. Experiments on three long-form video understanding benchmarks demonstrate that DIG consistently outperforms existing baselines and robustly improves LMM performance, even when scaling the input frame count to 256.
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            <a href="https://www.alphaxiv.org/abs/2512.03918v1" target="_blank" rel="noopener noreferrer">
                UniMo：基于自回归框架统一二维视频与三维人体运动
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            UniMo: Unifying 2D Video and 3D Human Motion with an Autoregressive Framework
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Youxin Pang, Yong Zhang, Ruizhi Shao, Xiang Deng, Feng Gao, Xu Xiaoming, Xiaomin...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域（2D视频和3D人体运动）的统一建模，属于纯粹的视觉或多模态研究方向。虽然标题提到“统一”框架，但内容明显聚焦于视觉数据模态，与推荐系统、搜索或广告的核心技术栈（用户行为序列、特征工程、排序模型等）缺乏直接关联。即使考虑VLM类比，该工作处理的是视觉模态内部统一，而非推荐系统所需的异构数据（如用户画像、上下文特征、物品属性等）跨模态建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 16:03:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03918v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03918v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We propose UniMo, an innovative autoregressive model for joint modeling of 2D human videos and 3D human motions within a unified framework, enabling simultaneous generation and understanding of these two modalities for the first time. Current methods predominantly focus on generating one modality given another as the condition or integrating either of them with other modalities such as text and audio. Unifying 2D videos and 3D motions for simultaneous optimization and generation remains largely unexplored, presenting significant challenges due to their substantial structural and distributional differences. Inspired by the LLM's ability to unify different modalities, our method models videos and 3D motions as a unified tokens sequence, utilizing separate embedding layers to mitigate distribution gaps. Additionally, we devise a sequence modeling strategy that integrates two distinct tasks within a single framework, proving the effectiveness of unified modeling. Moreover, to efficiently align with visual tokens and preserve 3D spatial information, we design a novel 3D motion tokenizer with a temporal expansion strategy, using a single VQ-VAE to produce quantized motion tokens. It features multiple expert decoders that handle body shapes, translation, global orientation, and body poses for reliable 3D motion reconstruction. Extensive experiments demonstrate that our method simultaneously generates corresponding videos and motions while performing accurate motion capture. This work taps into the capacity of LLMs to fuse diverse data types, paving the way for integrating human-centric information into existing models and potentially enabling multimodal, controllable joint modeling of humans, objects, and scenes.
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            <a href="https://www.alphaxiv.org/abs/2512.03905v1" target="_blank" rel="noopener noreferrer">
                基于帧间时空对应关系的零样本视频翻译与编辑
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            <i class="fa fa-star mr-1"></i>2/10
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            Zero-Shot Video Translation and Editing with Frame Spatial-Temporal Correspondence
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuai Yang, Junxin Lin, Yifan Zhou, Ziwei Liu, Chen Change Loy
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要涉及视频处理技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然零样本学习和编辑技术可能在某些内容理解场景中有潜在应用，但论文标题明确聚焦于视频翻译和编辑，这更偏向AIGC和内容生成方向，而非排名、检索或用户行为建模等核心关注点。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 15:51:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03905v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03905v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The remarkable success in text-to-image diffusion models has motivated extensive investigation of their potential for video applications. Zero-shot techniques aim to adapt image diffusion models for videos without requiring further model training. Recent methods largely emphasize integrating inter-frame correspondence into attention mechanisms. However, the soft constraint applied to identify the valid features to attend is insufficient, which could lead to temporal inconsistency. In this paper, we present FRESCO, which integrates intra-frame correspondence with inter-frame correspondence to formulate a more robust spatial-temporal constraint. This enhancement ensures a consistent transformation of semantically similar content between frames. Our method goes beyond attention guidance to explicitly optimize features, achieving high spatial-temporal consistency with the input video, significantly enhancing the visual coherence of manipulated videos. We verify FRESCO adaptations on two zero-shot tasks of video-to-video translation and text-guided video editing. Comprehensive experiments demonstrate the effectiveness of our framework in generating high-quality, coherent videos, highlighting a significant advance over current zero-shot methods.
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            <a href="https://www.alphaxiv.org/abs/2512.03796v1" target="_blank" rel="noopener noreferrer">
                LSRS：用于视觉自回归建模的潜在尺度拒绝采样
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            LSRS: Latent Scale Rejection Sampling for Visual Autoregressive Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hong-Kai Zheng, Piji Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于视觉自回归建模的采样方法，属于纯粹的计算机视觉技术。虽然自回归建模是LLM的核心技术，但该论文明确限定于视觉领域，没有提及任何与推荐系统、搜索或广告相关的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 13:44:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03796v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03796v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Visual Autoregressive (VAR) modeling approach for image generation proposes autoregressive processing across hierarchical scales, decoding multiple tokens per scale in parallel. This method achieves high-quality generation while accelerating synthesis. However, parallel token sampling within a scale may lead to structural errors, resulting in suboptimal generated images. To mitigate this, we propose Latent Scale Rejection Sampling (LSRS), a method that progressively refines token maps in the latent scale during inference to enhance VAR models. Our method uses a lightweight scoring model to evaluate multiple candidate token maps sampled at each scale, selecting the high-quality map to guide subsequent scale generation. By prioritizing early scales critical for structural coherence, LSRS effectively mitigates autoregressive error accumulation while maintaining computational efficiency. Experiments demonstrate that LSRS significantly improves VAR's generation quality with minimal additional computational overhead. For the VAR-d30 model, LSRS increases the inference time by merely 1% while reducing its FID score from 1.95 to 1.78. When the inference time is increased by 15%, the FID score can be further reduced to 1.66. LSRS offers an efficient test-time scaling solution for enhancing VAR-based generation.
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            <a href="https://www.alphaxiv.org/abs/2512.03724v1" target="_blank" rel="noopener noreferrer">
                PosA-VLA：通过姿态条件锚点注意力增强动作生成
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            PosA-VLA: Enhancing Action Generation via Pose-Conditioned Anchor Attention
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziwen Li, Xin Wang, Hanlue Zhang, Runnan Chen, Runqi Lin, Xiao He, Han Huang, Ya...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于动作生成，这属于计算机视觉或运动生成领域，而非推荐系统、搜索或广告的核心技术。虽然提到了注意力机制，但未明确展示在推荐、搜索或广告中的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 12:14:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03724v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03724v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    The Vision-Language-Action (VLA) models have demonstrated remarkable performance on embodied tasks and shown promising potential for real-world applications. However, current VLAs still struggle to produce consistent and precise target-oriented actions, as they often generate redundant or unstable motions along trajectories, limiting their applicability in time-sensitive scenarios.In this work, we attribute these redundant actions to the spatially uniform perception field of existing VLAs, which causes them to be distracted by target-irrelevant objects, especially in complex environments.To address this issue, we propose an efficient PosA-VLA framework that anchors visual attention via pose-conditioned supervision, consistently guiding the model's perception toward task-relevant regions. The pose-conditioned anchor attention mechanism enables the model to better align instruction semantics with actionable visual cues, thereby improving action generation precision and efficiency. Moreover, our framework adopts a lightweight architecture and requires no auxiliary perception modules (e.g., segmentation or grounding networks), ensuring efficient inference. Extensive experiments verify that our method executes embodied tasks with precise and time-efficient behavior across diverse robotic manipulation benchmarks and shows robust generalization in a variety of challenging environments.
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            <a href="https://www.alphaxiv.org/abs/2512.03701v1" target="_blank" rel="noopener noreferrer">
                结构化不确定性相似度评分（SUSS）：学习一种概率化、可解释、感知性的图像间度量
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            Structured Uncertainty Similarity Score (SUSS): Learning a Probabilistic, Interpretable, Perceptual Metric Between Images
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Paula Seidler, Neill D. F. Campbell, Ivor J A Simpson
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像间的感知相似性度量，属于计算机视觉领域。虽然提到了概率化和可解释性，但其核心应用场景是图像比较，与推荐系统、搜索或广告中的异构数据处理、用户序列建模或LLM应用没有直接关联。即使考虑VLM类比，该工作并未明确涉及多模态统一建模或推荐/搜索场景的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:48:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03701v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03701v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Perceptual similarity scores that align with human vision are critical for both training and evaluating computer vision models. Deep perceptual losses, such as LPIPS, achieve good alignment but rely on complex, highly non-linear discriminative features with unknown invariances, while hand-crafted measures like SSIM are interpretable but miss key perceptual properties. We introduce the Structured Uncertainty Similarity Score (SUSS); it models each image through a set of perceptual components, each represented by a structured multivariate Normal distribution. These are trained in a generative, self-supervised manner to assign high likelihood to human-imperceptible augmentations. The final score is a weighted sum of component log-probabilities with weights learned from human perceptual datasets. Unlike feature-based methods, SUSS learns image-specific linear transformations of residuals in pixel space, enabling transparent inspection through decorrelated residuals and sampling. SUSS aligns closely with human perceptual judgments, shows strong perceptual calibration across diverse distortion types, and provides localized, interpretable explanations of its similarity assessments. We further demonstrate stable optimization behavior and competitive performance when using SUSS as a perceptual loss for downstream imaging tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.03590v1" target="_blank" rel="noopener noreferrer">
                超越边界帧：面向上下文感知视频插值的音频-视觉语义引导
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            Beyond Boundary Frames: Audio-Visual Semantic Guidance for Context-Aware Video Interpolation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuchen Deng, Xiuyang Wu, Hai-Tao Zheng, Jie Wang, Feidiao Yang, Yuxing Han
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频插值技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈无直接关联。虽然标题提及“上下文感知”和“语义引导”，但这些概念在视频处理中的具体应用（如音频-视觉融合）与异构数据处理（如用户序列与上下文特征）的VLM类比存在本质差异，缺乏明确的RecSys/Search/Ads应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:22:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03590v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03590v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Handling fast, complex, and highly non-linear motion patterns has long posed challenges for video frame interpolation. Although recent diffusion-based approaches improve upon traditional optical-flow-based methods, they still struggle to cover diverse application scenarios and often fail to produce sharp, temporally consistent frames in fine-grained motion tasks such as audio-visual synchronized interpolation. To address these limitations, we introduce BBF (Beyond Boundary Frames), a context-aware video frame interpolation framework, which could be guided by audio/visual semantics. First, we enhance the input design of the interpolation model so that it can flexibly handle multiple conditional modalities, including text, audio, images, and video. Second, we propose a decoupled multimodal fusion mechanism that sequentially injects different conditional signals into a DiT backbone. Finally, to maintain the generation abilities of the foundation model, we adopt a progressive multi-stage training paradigm, where the start-end frame difference embedding is used to dynamically adjust both the data sampling and the loss weighting. Extensive experimental results demonstrate that BBF outperforms specialized state-of-the-art methods on both generic interpolation and audio-visual synchronized interpolation tasks, establishing a unified framework for video frame interpolation under coordinated multi-channel conditioning.
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            <a href="https://www.alphaxiv.org/abs/2512.03553v1" target="_blank" rel="noopener noreferrer">
                直播中的动态内容审核：结合监督分类与多模态大语言模型增强的相似性匹配
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            Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wei Chee Yew, Hailun Xu, Sanjay Saha, Xiaotian Fan, Hiok Hian Ong, David Yuchen ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注内容审核，属于安全/隐私/伦理范畴，这在您的关注点中被明确列为不相关主题。虽然提到了MLLM（多模态大语言模型）技术，但其应用场景（内容审核）与您的核心关注领域（推荐系统、搜索、广告的排名与建模）不直接相关，且没有明确说明该技术如何应用于这些领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 08:20:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03553v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03553v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Content moderation remains a critical yet challenging task for large-scale user-generated video platforms, especially in livestreaming environments where moderation must be timely, multimodal, and robust to evolving forms of unwanted content. We present a hybrid moderation framework deployed at production scale that combines supervised classification for known violations with reference-based similarity matching for novel or subtle cases. This hybrid design enables robust detection of both explicit violations and novel edge cases that evade traditional classifiers. Multimodal inputs (text, audio, visual) are processed through both pipelines, with a multimodal large language model (MLLM) distilling knowledge into each to boost accuracy while keeping inference lightweight. In production, the classification pipeline achieves 67% recall at 80% precision, and the similarity pipeline achieves 76% recall at 80% precision. Large-scale A/B tests show a 6-8% reduction in user views of unwanted livestreams}. These results demonstrate a scalable and adaptable approach to multimodal content governance, capable of addressing both explicit violations and emerging adversarial behaviors.
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                V-ITI：通过视觉推理时间干预缓解多模态大语言模型中的幻觉问题
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            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nan Sun, Zhenyu Zhang, Xixun Lin, Kun Wang, Yanmin Shang, Naibin Gu, Shuohuan Wa...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态大语言模型中的幻觉缓解问题，这属于纯粹的NLP/多模态评估范畴，与推荐系统、搜索或广告的核心技术进展无关。虽然提到了多模态模型，但焦点是幻觉缓解而非异构数据统一建模，且未展示在RecSys/Search/Ads领域的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 08:03:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03542v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03542v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Large Language Models (MLLMs) excel in numerous vision-language tasks yet suffer from hallucinations, producing content inconsistent with input visuals, that undermine reliability in precision-sensitive domains. This issue stems from a fundamental problem of visual neglect, where models fail to adequately prioritize input images. Existing methods typically alleviate hallucinations by intervening in the attention score or output logits, focusing on "how to intervene" but overlooking the prerequisite "when to intervene", which leads to the "over-intervention" problem and subsequently introduces new hallucinations and unnecessary computational overhead. To address this gap, we first investigate the mechanism of visual neglect and reveal it can be accurately detected via head-level activation patterns in MLLMs. We thus propose V-ITI, a lightweight visual inference-time intervention framework integrating a Visual Neglect Detector that identifies visual neglect via head-level discriminative probes and a Visual Recall Intervenor that modulates activations with prestored visual activation information only when the visual neglect is detected. Extensive experiments across eight benchmarks and different MLLM families demonstrate that V-ITI consistently mitigates vision-related hallucinations while preserving general task performance.
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            <a href="https://www.alphaxiv.org/abs/2512.03534v1" target="_blank" rel="noopener noreferrer">
                重新思考文本到视觉生成中推理时缩放的提示设计
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Rethinking Prompt Design for Inference-time Scaling in Text-to-Visual Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Subin Kim, Sangwoo Mo, Mamshad Nayeem Rizve, Yiran Xu, Difan Liu, Jinwoo Shin, T...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本到视觉生成的提示设计优化，属于AIGC和内容生成领域，与您的核心关注点（推荐系统、搜索、广告）的直接关联较弱。虽然提示设计技术可能间接影响某些应用，但论文本身未明确涉及RecSys/Search/Ads的具体应用场景或技术迁移潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 07:54:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03534v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03534v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Achieving precise alignment between user intent and generated visuals remains a central challenge in text-to-visual generation, as a single attempt often fails to produce the desired output. To handle this, prior approaches mainly scale the visual generation process (e.g., increasing sampling steps or seeds), but this quickly leads to a quality plateau. This limitation arises because the prompt, crucial for guiding generation, is kept fixed. To address this, we propose Prompt Redesign for Inference-time Scaling, coined PRIS, a framework that adaptively revises the prompt during inference in response to the scaled visual generations. The core idea of PRIS is to review the generated visuals, identify recurring failure patterns across visuals, and redesign the prompt accordingly before regenerating the visuals with the revised prompt. To provide precise alignment feedback for prompt revision, we introduce a new verifier, element-level factual correction, which evaluates the alignment between prompt attributes and generated visuals at a fine-grained level, achieving more accurate and interpretable assessments than holistic measures. Extensive experiments on both text-to-image and text-to-video benchmarks demonstrate the effectiveness of our approach, including a 15% gain on VBench 2.0. These results highlight that jointly scaling prompts and visuals is key to fully leveraging scaling laws at inference-time. Visualizations are available at the website: https://subin-kim-cv.github.io/PRIS.
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            <a href="https://www.alphaxiv.org/abs/2512.03500v1" target="_blank" rel="noopener noreferrer">
                EEA：用于长视频理解的探索-利用智能体
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            EEA: Exploration-Exploitation Agent for Long Video Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Te Yang, Xiangyu Zhu, Bo Wang, Quan Chen, Peng Jiang, Zhen Lei
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注长视频理解，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术焦点没有直接关联。虽然探索-利用机制在推荐系统中具有潜在应用，但论文标题明确限定于视频理解，缺乏明确的跨模态或推荐系统应用指向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 06:48:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03500v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03500v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Long-form video understanding requires efficient navigation of extensive visual data to pinpoint sparse yet critical information. Current approaches to longform video understanding either suffer from severe computational overhead due to dense preprocessing, or fail to effectively balance exploration and exploitation, resulting in incomplete information coverage and inefficiency. In this work, we introduce EEA, a novel video agent framework that archives exploration-exploitation balance through semantic guidance with hierarchical tree search process. EEA autonomously discovers and dynamically updates task-relevant semantic queries, and collects video frames closely matched to these queries as semantic anchors. During the tree search process, instead of uniform expansion, EEA preferentially explores semantically relevant frames while ensuring sufficient coverage within unknown segments. Moreover, EEA adaptively combines intrinsic rewards from visionlanguage models (VLMs) with semantic priors by explicitly modeling uncertainty to achieve stable and precise evaluation of video segments. Experiments across various long-video benchmarks validate the superior performance and computational efficiency of our proposed method.
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            <a href="https://www.alphaxiv.org/abs/2512.03479v1" target="_blank" rel="noopener noreferrer">
                面向教学视频的对象中心化理解
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Towards Object-centric Understanding for Instructional Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenliang Guo, Yu Kong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于视频理解，特别是教学视频中的对象识别与分析，这属于计算机视觉领域。虽然视频理解技术可能间接应用于推荐系统中的内容理解（如视频推荐），但标题未明确涉及推荐、搜索或广告的核心问题，也未提及LLM、Transformer架构或异构数据建模等关键技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 06:14:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03479v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03479v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Understanding procedural activities is crucial for developing future assistive AI that can reason about complex real-world tasks. Existing action-centric methods struggle with the flexibility of real procedures, where step order varies depending on object states. In this work, we propose to shift the focus to an object-centric paradigm by regarding actions as mechanisms that drive state transitions. To advance this direction, we introduce Object-IVQA, a long-form instructional video benchmark with 107 videos and 514 open-ended question-answer pairs annotated with temporally grounded evidence. The benchmark evaluates four dimensions of object-centric reasoning, including state evolution, precondition verification, counterfactual reasoning and mistake recognition. We further propose an agent framework that orchestrates object-centric planning, perception, analysis and generation tools, enabling explicit evidence retrieval and multi-hop reasoning across disjoint segments. Experiments show that existing large vision-language models struggle in object-level recognition and reasoning, whereas our framework achieves substantially improvement.
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            <a href="https://www.alphaxiv.org/abs/2512.03474v1" target="_blank" rel="noopener noreferrer">
                基于动作效果建模的程序性错误检测
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Procedural Mistake Detection via Action Effect Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenliang Guo, Yujiang Pu, Yu Kong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及程序性错误检测和动作效果建模，这属于过程监控或机器人控制领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然动作效果建模可能涉及序列建模，但论文没有表明与Transformer架构、LLM技术或推荐/搜索/广告应用有任何联系，因此相关性很低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:56:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03474v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03474v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Mistake detection in procedural tasks is essential for building intelligent systems that support learning and task execution. Existing approaches primarily analyze how an action is performed, while overlooking what it produces, i.e., the \textbf{action effect}. Yet many errors manifest not in the execution itself but in the resulting outcome, such as an unintended object state or incorrect spatial arrangement. To address this gap, we propose Action Effect Modeling (AEM), a unified framework that jointly captures action execution and its outcomes through a probabilistic formulation. AEM first identifies the outcome of an action by selecting the most informative effect frame based on semantic relevance and visual quality. It then extracts complementary cues from visual grounding and symbolic scene graphs, aligning them in a shared latent space to form robust effect-aware representations. To detect mistakes, we further design a prompt-based detector that incorporates task-specific prompts and aligns each action segment with its intended execution semantics. Our approach achieves state-of-the-art performance on the EgoPER and CaptainCook4D benchmarks under the challenging one-class classification (OCC) setting. These results demonstrate that modeling both execution and outcome yields more reliable mistake detection, and highlight the potential of effect-aware representations to benefit a broader range of downstream applications.
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            <a href="https://www.alphaxiv.org/abs/2512.03451v1" target="_blank" rel="noopener noreferrer">
                GalaxyDiT：基于引导对齐与自适应代理的扩散Transformer高效视频生成
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            GalaxyDiT: Efficient Video Generation with Guidance Alignment and Adaptive Proxy in Diffusion Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiye Song, Steve Dai, Ben Keller, Brucek Khailany
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频生成领域，属于AIGC/内容生成范畴，与RecSys/Search/Ads的核心排序、推荐任务无直接关联。虽然涉及Transformer架构，但其应用场景（视频生成）不在当前关注范围内，且未提及在推荐或广告系统中的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:08:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03451v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03451v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Diffusion models have revolutionized video generation, becoming essential tools in creative content generation and physical simulation. Transformer-based architectures (DiTs) and classifier-free guidance (CFG) are two cornerstones of this success, enabling strong prompt adherence and realistic video quality. Despite their versatility and superior performance, these models require intensive computation. Each video generation requires dozens of iterative steps, and CFG doubles the required compute. This inefficiency hinders broader adoption in downstream applications. We introduce GalaxyDiT, a training-free method to accelerate video generation with guidance alignment and systematic proxy selection for reuse metrics. Through rank-order correlation analysis, our technique identifies the optimal proxy for each video model, across model families and parameter scales, thereby ensuring optimal computational reuse. We achieve $1.87\times$ and $2.37\times$ speedup on Wan2.1-1.3B and Wan2.1-14B with only 0.97% and 0.72% drops on the VBench-2.0 benchmark. At high speedup rates, our approach maintains superior fidelity to the base model, exceeding prior state-of-the-art approaches by 5 to 10 dB in peak signal-to-noise ratio (PSNR).
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            <a href="https://www.alphaxiv.org/abs/2512.03450v1" target="_blank" rel="noopener noreferrer">
                KeyPointDiffuser：基于潜在扩散模型的无监督三维关键点学习
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            KeyPointDiffuser: Unsupervised 3D Keypoint Learning via Latent Diffusion Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rhys Newbury, Juyan Zhang, Tin Tran, Hanna Kurniawati, Dana Kulić
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于3D关键点学习和扩散模型，属于计算机视觉和生成模型领域。虽然扩散模型是LLM相关技术，但论文明确针对3D视觉应用，没有提及推荐系统、搜索或广告的潜在应用场景，与当前关注点直接相关性较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:08:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03450v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03450v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Understanding and representing the structure of 3D objects in an unsupervised manner remains a core challenge in computer vision and graphics. Most existing unsupervised keypoint methods are not designed for unconditional generative settings, restricting their use in modern 3D generative pipelines; our formulation explicitly bridges this gap. We present an unsupervised framework for learning spatially structured 3D keypoints from point cloud data. These keypoints serve as a compact and interpretable representation that conditions an Elucidated Diffusion Model (EDM) to reconstruct the full shape. The learned keypoints exhibit repeatable spatial structure across object instances and support smooth interpolation in keypoint space, indicating that they capture geometric variation. Our method achieves strong performance across diverse object categories, yielding a 6 percentage-point improvement in keypoint consistency compared to prior approaches.
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            <a href="https://www.alphaxiv.org/abs/2512.03465v1" target="_blank" rel="noopener noreferrer">
                针对TraceTarnish的调优：技术、趋势与可测试特性的验证
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
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        <div class="mb-2 text-base text-gray-700">
            Tuning for TraceTarnish: Techniques, Trends, and Testing Tangible Traits
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Robert Dilworth
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题涉及系统性能调优和测试，但未明确指向推荐系统、搜索或广告的核心技术。标题中的'TraceTarnish'可能指系统性能问题，但缺乏与LLM、Transformer架构、多模态建模或直接应用场景的明确关联。无法推断其在当前关注领域的具体应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:39:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03465v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03465v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this study, we more rigorously evaluated our attack script $\textit{TraceTarnish}$, which leverages adversarial stylometry principles to anonymize the authorship of text-based messages. To ensure the efficacy and utility of our attack, we sourced, processed, and analyzed Reddit comments--comments that were later alchemized into $\textit{TraceTarnish}$ data--to gain valuable insights. The transformed $\textit{TraceTarnish}$ data was then further augmented by $\textit{StyloMetrix}$ to manufacture stylometric features--features that were culled using the Information Gain criterion, leaving only the most informative, predictive, and discriminative ones. Our results found that function words and function word types ($L\_FUNC\_A$ $\&$ $L\_FUNC\_T$); content words and content word types ($L\_CONT\_A$ $\&$ $L\_CONT\_T$); and the Type-Token Ratio ($ST\_TYPE\_TOKEN\_RATIO\_LEMMAS$) yielded significant Information-Gain readings. The identified stylometric cues--function-word frequencies, content-word distributions, and the Type-Token Ratio--serve as reliable indicators of compromise (IoCs), revealing when a text has been deliberately altered to mask its true author. Similarly, these features could function as forensic beacons, alerting defenders to the presence of an adversarial stylometry attack; granted, in the absence of the original message, this signal may go largely unnoticed, as it appears to depend on a pre- and post-transformation comparison. "In trying to erase a trace, you often imprint a larger one." Armed with this understanding, we framed $\textit{TraceTarnish}$'s operations and outputs around these five isolated features, using them to conceptualize and implement enhancements that further strengthen the attack.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04048v1" target="_blank" rel="noopener noreferrer">
                Stable Signer：层次化手语生成模型
            </a>
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            Stable Signer: Hierarchical Sign Language Generative Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sen Fang, Yalin Feng, Hongbin Zhong, Yanxin Zhang, Dimitris N. Metaxas
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于手语生成，属于特定领域的模态生成任务，与推荐系统、搜索或广告的核心技术领域没有直接关联。虽然涉及生成模型，但缺乏明确的跨模态建模或序列建模方法，无法直接应用于异构数据处理或推荐/搜索场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:33:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04048v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04048v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span></div>
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                    Sign Language Production (SLP) is the process of converting the complex input text into a real video. Most previous works focused on the Text2Gloss, Gloss2Pose, Pose2Vid stages, and some concentrated on Prompt2Gloss and Text2Avatar stages. However, this field has made slow progress due to the inaccuracy of text conversion, pose generation, and the rendering of poses into real human videos in these stages, resulting in gradually accumulating errors. Therefore, in this paper, we streamline the traditional redundant structure, simplify and optimize the task objective, and design a new sign language generative model called Stable Signer. It redefines the SLP task as a hierarchical generation end-to-end task that only includes text understanding (Prompt2Gloss, Text2Gloss) and Pose2Vid, and executes text understanding through our proposed new Sign Language Understanding Linker called SLUL, and generates hand gestures through the named SLP-MoE hand gesture rendering expert block to end-to-end generate high-quality and multi-style sign language videos. SLUL is trained using the newly developed Semantic-Aware Gloss Masking Loss (SAGM Loss). Its performance has improved by 48.6% compared to the current SOTA generation methods.
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            <a href="https://www.alphaxiv.org/abs/2512.03943v1" target="_blank" rel="noopener noreferrer">
                说谎仅在伊斯兰教中是有罪的吗？探索多语言大语言模型在主要宗教中的宗教偏见
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        <div class="mb-2 text-base text-gray-700">
            Is Lying Only Sinful in Islam? Exploring Religious Bias in Multilingual Large Language Models Across Major Religions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kazi Abrab Hossain, Jannatul Somiya Mahmud, Maria Hossain Tuli, Anik Mitra, S. M...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题关注多语言大语言模型的宗教偏见评估，属于纯粹的NLP评估基准研究，与推荐系统、搜索或广告的核心技术进展、LLM应用或Transformer架构改进无关。论文没有展示任何在推荐/搜索/广告领域的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 16:38:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03943v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03943v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.HC</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While recent developments in large language models have improved bias detection and classification, sensitive subjects like religion still present challenges because even minor errors can result in severe misunderstandings. In particular, multilingual models often misrepresent religions and have difficulties being accurate in religious contexts. To address this, we introduce BRAND: Bilingual Religious Accountable Norm Dataset, which focuses on the four main religions of South Asia: Buddhism, Christianity, Hinduism, and Islam, containing over 2,400 entries, and we used three different types of prompts in both English and Bengali. Our results indicate that models perform better in English than in Bengali and consistently display bias toward Islam, even when answering religion-neutral questions. These findings highlight persistent bias in multilingual models when similar questions are asked in different languages. We further connect our findings to the broader issues in HCI regarding religion and spirituality.
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            <a href="https://www.alphaxiv.org/abs/2512.03903v1" target="_blank" rel="noopener noreferrer">
                BERnaT：用于表示自然文本多样性的巴斯克语编码器
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            BERnaT: Basque Encoders for Representing Natural Textual Diversity
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ekhi Azurmendi, Joseba Fernandez de Landa, Jaione Bengoetxea, Maite Heredia, Jul...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于巴斯克语这一特定语言的文本编码器开发，属于语言特定的NLP研究。它不涉及推荐系统、搜索或广告领域的核心进展，也不包含可能应用于这些领域的LLM技术、Transformer架构改进或异构数据统一建模方法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 15:50:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03903v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03903v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Language models depend on massive text corpora that are often filtered for quality, a process that can unintentionally exclude non-standard linguistic varieties, reduce model robustness and reinforce representational biases. In this paper, we argue that language models should aim to capture the full spectrum of language variation (dialectal, historical, informal, etc.) rather than relying solely on standardized text. Focusing on Basque, a morphologically rich and low-resource language, we construct new corpora combining standard, social media, and historical sources, and pre-train the BERnaT family of encoder-only models in three configurations: standard, diverse, and combined. We further propose an evaluation framework that separates Natural Language Understanding (NLU) tasks into standard and diverse subsets to assess linguistic generalization. Results show that models trained on both standard and diverse data consistently outperform those trained on standard corpora, improving performance across all task types without compromising standard benchmark accuracy. These findings highlight the importance of linguistic diversity in building inclusive, generalizable language models.
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            <a href="https://www.alphaxiv.org/abs/2512.03838v1" target="_blank" rel="noopener noreferrer">
                大型语言模型中基于指南的医学推理训练与评估
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            Training and Evaluation of Guideline-Based Medical Reasoning in LLMs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Michael Staniek, Artem Sokolov, Stefan Riezler
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域的指南推理应用，属于'Irrelevant Topics'中明确排除的'Medical, Biology, Chemistry, Physics or other domain-specific applications'范畴。虽然涉及LLM训练评估，但核心是医学特定应用，与推荐系统、搜索或广告领域无直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:39:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03838v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03838v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Machine learning for early prediction in medicine has recently shown breakthrough performance, however, the focus on improving prediction accuracy has led to a neglect of faithful explanations that are required to gain the trust of medical practitioners. The goal of this paper is to teach LLMs to follow medical consensus guidelines step-by-step in their reasoning and prediction process. Since consensus guidelines are ubiquitous in medicine, instantiations of verbalized medical inference rules to electronic health records provide data for fine-tuning LLMs to learn consensus rules and possible exceptions thereof for many medical areas. Consensus rules also enable an automatic evaluation of the model's inference process regarding its derivation correctness (evaluating correct and faithful deduction of a conclusion from given premises) and value correctness (comparing predicted values against real-world measurements). We exemplify our work using the complex Sepsis-3 consensus definition. Our experiments show that small fine-tuned models outperform one-shot learning of considerably larger LLMs that are prompted with the explicit definition and models that are trained on medical texts including consensus definitions. Since fine-tuning on verbalized rule instantiations of a specific medical area yields nearly perfect derivation correctness for rules (and exceptions) on unseen patient data in that area, the bottleneck for early prediction is not out-of-distribution generalization, but the orthogonal problem of generalization into the future by forecasting sparsely and irregularly sampled clinical variables. We show that the latter results can be improved by integrating the output representations of a time series forecasting model with the LLM in a multimodal setup.
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            <a href="https://www.alphaxiv.org/abs/2512.03818v1" target="_blank" rel="noopener noreferrer">
                提升人类与机器编码的对齐度：心理学中构念识别的提示工程实证评估
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        <div class="mb-2 text-base text-gray-700">
            Improving Alignment Between Human and Machine Codes: An Empirical Assessment of Prompt Engineering for Construct Identification in Psychology
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kylie L. Anglin, Stephanie Milan, Brittney Hernandez, Claudia Ventura
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于心理学领域的构念识别，属于心理学特定领域应用，与用户当前关注的推荐系统、搜索、广告、LLM技术或Transformer架构等核心领域完全无关。标题中提到的“提示工程”虽然涉及LLM技术，但应用场景被严格限定在心理学研究这一无关领域，不具备向RecSys/Search/Ads迁移的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:07:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03818v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03818v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Due to their architecture and vast pre-training data, large language models (LLMs) demonstrate strong text classification performance. However, LLM output - here, the category assigned to a text - depends heavily on the wording of the prompt. While literature on prompt engineering is expanding, few studies focus on classification tasks, and even fewer address domains like psychology, where constructs have precise, theory-driven definitions that may not be well represented in pre-training data. We present an empirical framework for optimizing LLM performance for identifying constructs in texts via prompt engineering. We experimentally evaluate five prompting strategies --codebook-guided empirical prompt selection, automatic prompt engineering, persona prompting, chain-of-thought reasoning, and explanatory prompting - with zero-shot and few-shot classification. We find that persona, chain-of-thought, and explanations do not fully address performance loss accompanying a badly worded prompt. Instead, the most influential features of a prompt are the construct definition, task framing, and, to a lesser extent, the examples provided. Across three constructs and two models, the classifications most aligned with expert judgments resulted from a few-shot prompt combining codebook-guided empirical prompt selection with automatic prompt engineering. Based on our findings, we recommend that researchers generate and evaluate as many prompt variants as feasible, whether human-crafted, automatically generated, or ideally both, and select prompts and examples based on empirical performance in a training dataset, validating the final approach in a holdout set. This procedure offers a practical, systematic, and theory-driven method for optimizing LLM prompts in settings where alignment with expert judgment is critical.
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            <a href="https://www.alphaxiv.org/abs/2512.03746v1" target="_blank" rel="noopener noreferrer">
                基于编程视觉的思考：迈向图像思考的统一视角
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        <div class="mb-2 text-base text-gray-700">
            Thinking with Programming Vision: Towards a Unified View for Thinking with Images
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zirun Guo, Minjie Hong, Feng Zhang, Kai Jia, Tao Jin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于视觉与思考的交叉领域，属于纯粹的视觉认知研究方向。标题中提到的'Thinking with Images'表明其核心是视觉认知处理，与推荐系统、搜索或广告领域没有直接关联，也不涉及LLM技术或Transformer架构在相关领域的应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 12:44:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03746v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03746v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal large language models (MLLMs) that think with images can interactively use tools to reason about visual inputs, but current approaches often rely on a narrow set of tools with limited real-world necessity and scalability. In this work, we first reveal a critical and previously overlooked weakness: even state-of-the-art MLLMs are surprisingly brittle, showing significant performance degradation on images with simple orientation changes or natural corruptions, underscoring the need for more robust tool-based reasoning. To address this, we propose CodeVision, a flexible and scalable code-as-tool framework where the model generates code as a universal interface to invoke any image operation, moving beyond fixed tool registries. We train our model using a two-stage methodology, beginning with Supervised Fine-Tuning (SFT) on a high-quality dataset curated for complex, multi-turn tool composition and error recovery, followed by Reinforcement Learning (RL) with a novel and dense process reward function to encourage strategic and efficient tool use. To facilitate this research, we construct new SFT and RL datasets and introduce a challenging new benchmark suite designed to rigorously evaluate robustness to orientation changes and multi-tool reasoning. Experiments on Qwen2.5-VL and Qwen3-VL series show that our approach significantly improves model performance and fosters emergent capabilities such as flexible tool composition, efficient chained execution, and robust error recovery from runtime feedback. Code is available at https://github.com/ByteDance-BandAI/CodeVision.
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            <a href="https://www.alphaxiv.org/abs/2512.03688v1" target="_blank" rel="noopener noreferrer">
                AITutor-EvalKit：探索AI导师的能力
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        <div class="mb-2 text-base text-gray-700">
            AITutor-EvalKit: Exploring the Capabilities of AI Tutors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Numaan Naeem, Kaushal Kumar Maurya, Kseniia Petukhova, Ekaterina Kochmar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于AI教育导师的评估工具包，属于教育技术领域，与推荐系统、搜索或广告的核心技术进展无关。标题中没有任何元素表明该研究涉及LLM在推荐/搜索/广告中的应用、Transformer架构改进、异构数据统一建模等当前关注的技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:27:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03688v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03688v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    We present AITutor-EvalKit, an application that uses language technology to evaluate the pedagogical quality of AI tutors, provides software for demonstration and evaluation, as well as model inspection and data visualization. This tool is aimed at education stakeholders as well as *ACL community at large, as it supports learning and can also be used to collect user feedback and annotations.
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            <a href="https://www.alphaxiv.org/abs/2512.03672v1" target="_blank" rel="noopener noreferrer">
                评估大型语言模型的水科学与工程知识
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            Evaluating Hydro-Science and Engineering Knowledge of Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shiruo Hu, Wenbo Shan, Yingjia Li, Zhiqi Wan, Xinpeng Yu, Yunjia Qi, Haotian Xia...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于水科学与工程这一特定领域知识评估，属于您所列的无关主题中的“Medical, Biology, Chemistry, Physics or other domain-specific applications”。标题中未提及任何与推荐系统、搜索、广告、Transformer架构、多模态建模或LLM在这些领域应用相关的内容。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:01:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03672v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03672v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Hydro-Science and Engineering (Hydro-SE) is a critical and irreplaceable domain that secures human water supply, generates clean hydropower energy, and mitigates flood and drought disasters. Featuring multiple engineering objectives, Hydro-SE is an inherently interdisciplinary domain that integrates scientific knowledge with engineering expertise. This integration necessitates extensive expert collaboration in decision-making, which poses difficulties for intelligence. With the rapid advancement of large language models (LLMs), their potential application in the Hydro-SE domain is being increasingly explored. However, the knowledge and application abilities of LLMs in Hydro-SE have not been sufficiently evaluated. To address this issue, we propose the Hydro-SE LLM evaluation benchmark (Hydro-SE Bench), which contains 4,000 multiple-choice questions. Hydro-SE Bench covers nine subfields and enables evaluation of LLMs in aspects of basic conceptual knowledge, engineering application ability, and reasoning and calculation ability. The evaluation results on Hydro-SE Bench show that the accuracy values vary among 0.74 to 0.80 for commercial LLMs, and among 0.41 to 0.68 for small-parameter LLMs. While LLMs perform well in subfields closely related to natural and physical sciences, they struggle with domain-specific knowledge such as industry standards and hydraulic structures. Model scaling mainly improves reasoning and calculation abilities, but there is still great potential for LLMs to better handle problems in practical engineering application. This study highlights the strengths and weaknesses of LLMs for Hydro-SE tasks, providing model developers with clear training targets and Hydro-SE researchers with practical guidance for applying LLMs.
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            <a href="https://www.alphaxiv.org/abs/2512.03671v1" target="_blank" rel="noopener noreferrer">
                生成式AI实践、素养与鸿沟：意大利背景下的实证分析
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            Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Beatrice Savoldi, Giuseppe Attanasio, Olga Gorodetskaya, Marta Marchiori Manerba...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明这是一项关于生成式AI实践、素养和社会鸿沟的实证研究，属于社会科学、教育或政策研究范畴。它完全不涉及推荐系统、搜索或广告的技术核心进展，也不涉及LLM/Transformer架构改进或直接应用，而是聚焦于社会影响和素养调查，属于明确的非技术性话题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:01:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03671v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03671v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The rise of Artificial Intelligence (AI) language technologies, particularly generative AI (GenAI) chatbots accessible via conversational interfaces, is transforming digital interactions. While these tools hold societal promise, they also risk widening digital divides due to uneven adoption and low awareness of their limitations. This study presents the first comprehensive empirical mapping of GenAI adoption, usage patterns, and literacy in Italy, based on newly collected survey data from 1,906 Italian-speaking adults. Our findings reveal widespread adoption for both work and personal use, including sensitive tasks like emotional support and medical advice. Crucially, GenAI is supplanting other technologies to become a primary information source: this trend persists despite low user digital literacy, posing a risk as users struggle to recognize errors or misinformation. Moreover, we identify a significant gender divide -- particularly pronounced in older generations -- where women are half as likely to adopt GenAI and use it less frequently than men. While we find literacy to be a key predictor of adoption, it only partially explains this disparity, suggesting that other barriers are at play. Overall, our data provide granular insights into the multipurpose usage of GenAI, highlighting the dual need for targeted educational initiatives and further investigation into the underlying barriers to equitable participation that competence alone cannot explain.
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            <a href="https://www.alphaxiv.org/abs/2512.03643v1" target="_blank" rel="noopener noreferrer">
                光学上下文压缩本质上只是（糟糕的）自编码
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            Optical Context Compression Is Just (Bad) Autoencoding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ivan Yee Lee, Cheng Yang, Taylor Berg-Kirkpatrick
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及光学上下文压缩和自编码技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术无直接关联。标题暗示对特定压缩方法的批评，但未提及任何可应用于异构数据处理、Transformer架构改进或LLM在推荐/搜索领域的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 10:27:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03643v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03643v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    DeepSeek-OCR demonstrates that rendered text can be reconstructed with high fidelity from a small number of vision tokens. This finding has sparked excitement about vision-based context compression for language models. But the evaluation stops at reconstruction; whether these representations help language modeling remains untested. We test two assumptions implicit in the optical-compression narrative: that vision-based compression provides unique advantages for text reconstruction from compressed representations, and that DeepSeek-OCR's reconstruction results are evidence that vision-based compression will be useful for language modeling. Comparing their vision encoder against simple alternatives--parameter-free mean pooling and a learned hierarchical encoder--we find that these simple approaches match or surpass vision for reconstruction at matched compression ratios, and outperform it for language modeling--where vision-based compression fails to beat truncation. The excitement around optical context compression outpaces the evidence. Code and checkpoints are available at https://github.com/ivnle/bad-autoencoding
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            <a href="https://www.alphaxiv.org/abs/2512.03634v1" target="_blank" rel="noopener noreferrer">
                AlignCheck：一种用于事实一致性评估的语义开放域度量方法
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            AlignCheck: a Semantic Open-Domain Metric for Factual Consistency Assessment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ahmad Aghaebrahimian
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确关注事实一致性评估和度量方法，这属于纯粹的NLP评估基准主题，与您关注的推荐系统、搜索或广告领域的核心进展、LLM技术应用、Transformer架构改进或异构数据统一建模等方向完全无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 10:14:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03634v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03634v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Large Language Models have significantly advanced natural language processing tasks, but remain prone to generating incorrect or misleading but plausible arguments. This issue, known as hallucination, is particularly concerning in high-stakes domains like clinical applications, where factual inaccuracies can have severe consequences. Existing evaluation metrics fail to adequately assess factual consistency and lack interpretability, making diagnosing and mitigating errors difficult. We propose an interpretable framework for factual consistency assessment for in-domain and open-domain texts to address these limitations. Our approach decomposes text into atomic facts and introduces a flexible, schema-free methodology. Unlike previous methods with an absolute metric, we incorporate a weighted metric to enhance factual evaluation. Additionally, we propose a mechanism to control assessment complexity in intricate domains. We benchmark our approach on popular general and clinical datasets and release our code to support fact-aware model training in future research.
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            <a href="https://www.alphaxiv.org/abs/2512.03620v1" target="_blank" rel="noopener noreferrer">
                SELF：一种基于奇异值和特征值的鲁棒性大语言模型指纹识别方法
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            SELF: A Robust Singular Value and Eigenvalue Approach for LLM Fingerprinting
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hanxiu Zhang, Yue Zheng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及'LLM fingerprinting'（大语言模型指纹识别），这属于用户明确排除的'Irrelevant Topics'中的'Fingerprint'类别。虽然标题包含'LLM'和'Singular Value/Eigenvalue'（奇异值/特征值）等技术术语，但其核心研究目的（指纹识别）与用户关注的推荐系统、搜索、广告等核心领域进展、使能技术或直接应用完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:53:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03620v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03620v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    The protection of Intellectual Property (IP) in Large Language Models (LLMs) represents a critical challenge in contemporary AI research. While fingerprinting techniques have emerged as a fundamental mechanism for detecting unauthorized model usage, existing methods -- whether behavior-based or structural -- suffer from vulnerabilities such as false claim attacks or susceptible to weight manipulations. To overcome these limitations, we propose SELF, a novel intrinsic weight-based fingerprinting scheme that eliminates dependency on input and inherently resists false claims. SELF achieves robust IP protection through two key innovations: 1) unique, scalable and transformation-invariant fingerprint extraction via singular value and eigenvalue decomposition of LLM attention weights, and 2) effective neural network-based fingerprint similarity comparison based on few-shot learning and data augmentation. Experimental results demonstrate SELF maintains high IP infringement detection accuracy while showing strong robustness against various downstream modifications, including quantization, pruning, and fine-tuning attacks. Our code is available at https://github.com/HanxiuZhang/SELF_v2.
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            <a href="https://www.alphaxiv.org/abs/2512.03558v1" target="_blank" rel="noopener noreferrer">
                CartoMapQA：评估视觉语言模型在地图理解能力上的基础基准数据集
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            CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huy Quang Ung, Guillaume Habault, Yasutaka Nishimura, Hao Niu, Roberto Legaspi, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确表明其核心是评估视觉语言模型（VLM）在地图理解任务上的性能，属于纯粹的VLM评估基准研究。虽然提到了视觉语言模型，但该研究专注于地图这一特定视觉领域，与推荐系统、搜索或广告中的异构数据处理没有直接关联，也不涉及LLM在RecSys/Search/Ads中的直接应用或架构创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 08:25:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03558v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03558v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The rise of Visual-Language Models (LVLMs) has unlocked new possibilities for seamlessly integrating visual and textual information. However, their ability to interpret cartographic maps remains largely unexplored. In this paper, we introduce CartoMapQA, a benchmark specifically designed to evaluate LVLMs' understanding of cartographic maps through question-answering tasks. The dataset includes over 2000 samples, each composed of a cartographic map, a question (with open-ended or multiple-choice answers), and a ground-truth answer. These tasks span key low-, mid- and high-level map interpretation skills, including symbol recognition, embedded information extraction, scale interpretation, and route-based reasoning. Our evaluation of both open-source and proprietary LVLMs reveals persistent challenges: models frequently struggle with map-specific semantics, exhibit limited geospatial reasoning, and are prone to Optical Character Recognition (OCR)-related errors. By isolating these weaknesses, CartoMapQA offers a valuable tool for guiding future improvements in LVLM architectures. Ultimately, it supports the development of models better equipped for real-world applications that depend on robust and reliable map understanding, such as navigation, geographic search, and urban planning. Our source code and data are openly available to the research community at: https://github.com/ungquanghuy-kddi/CartoMapQA.git
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            <a href="https://www.alphaxiv.org/abs/2512.03373v1" target="_blank" rel="noopener noreferrer">
                LLM生成广告：从个性化对等到说服力优势
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            LLM-Generated Ads: From Personalization Parity to Persuasion Superiority
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Elyas Meguellati, Stefano Civelli, Lei Han, Abraham Bernstein, Shazia Sadiq, Gia...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于广告创意生成（'LLM-Generated Ads'），这属于明确的非相关主题（'Ads creative generation'）。虽然涉及个性化，但核心是内容生成而非排名或推荐系统，属于纯粹的LLM应用范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 02:13:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03373v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03373v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CY</span><span class="category-tag">cs.CL</span></div>
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                    As large language models (LLMs) become increasingly capable of generating persuasive content, understanding their effectiveness across different advertising strategies becomes critical. This paper presents a two-part investigation examining LLM-generated advertising through complementary lenses: (1) personality-based and (2) psychological persuasion principles. In our first study (n=400), we tested whether LLMs could generate personalized advertisements tailored to specific personality traits (openness and neuroticism) and how their performance compared to human experts. Results showed that LLM-generated ads achieved statistical parity with human-written ads (51.1% vs. 48.9%, p > 0.05), with no significant performance differences for matched personalities. Building on these insights, our second study (n=800) shifted focus from individual personalization to universal persuasion, testing LLM performance across four foundational psychological principles: authority, consensus, cognition, and scarcity. AI-generated ads significantly outperformed human-created content, achieving a 59.1% preference rate (vs. 40.9%, p < 0.001), with the strongest performance in authority (63.0%) and consensus (62.5%) appeals. Qualitative analysis revealed AI's advantage stems from crafting more sophisticated, aspirational messages and achieving superior visual-narrative coherence. Critically, this quality advantage proved robust: even after applying a 21.2 percentage point detection penalty when participants correctly identified AI-origin, AI ads still outperformed human ads, and 29.4% of participants chose AI content despite knowing its origin. These findings demonstrate LLMs' evolution from parity in personalization to superiority in persuasive storytelling, with significant implications for advertising practice given LLMs' near-zero marginal cost and time requirements compared to human experts.
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            <a href="https://www.alphaxiv.org/abs/2512.03340v1" target="_blank" rel="noopener noreferrer">
                PERCS：基于人物角色引导的可控生物医学摘要数据集
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            PERCS: Persona-Guided Controllable Biomedical Summarization Dataset
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rohan Charudatt Salvi, Chirag Chawla, Dhruv Jain, Swapnil Panigrahi, Md Shad Akh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及生物医学领域和摘要生成任务，这属于明确的无关主题（医学/生物学应用和纯摘要生成）。虽然提到了“可控生成”和“人物角色”概念，但这些在标题中被限定在生物医学上下文中，没有显示出与推荐系统、搜索或广告的潜在应用关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 01:13:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03340v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03340v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Automatic medical text simplification plays a key role in improving health literacy by making complex biomedical research accessible to diverse readers. However, most existing resources assume a single generic audience, overlooking the wide variation in medical literacy and information needs across user groups. To address this limitation, we introduce PERCS (Persona-guided Controllable Summarization), a dataset of biomedical abstracts paired with summaries tailored to four personas: Laypersons, Premedical Students, Non-medical Researchers, and Medical Experts. These personas represent different levels of medical literacy and information needs, emphasizing the need for targeted, audience-specific summarization. Each summary in PERCS was reviewed by physicians for factual accuracy and persona alignment using a detailed error taxonomy. Technical validation shows clear differences in readability, vocabulary, and content depth across personas. Along with describing the dataset, we benchmark four large language models on PERCS using automatic evaluation metrics that assess comprehensiveness, readability, and faithfulness, establishing baseline results for future research. The dataset, annotation guidelines, and evaluation materials are publicly available to support research on persona-specific communication and controllable biomedical summarization.
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            <a href="https://www.alphaxiv.org/abs/2512.03337v1" target="_blank" rel="noopener noreferrer">
                认知替代：Grokipedia的AI生成百科全书如何重构权威
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            Epistemic Substitution: How Grokipedia's AI-Generated Encyclopedia Restructures Authority
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aliakbar Mehdizadeh, Martin Hilbert
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题讨论AI生成内容对权威结构的影响，属于AIGC/内容生成领域，这是明确列出的无关主题。标题未涉及推荐系统、搜索或广告的核心算法、架构改进或应用，也没有展示任何在推荐/搜索/广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 01:05:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03337v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03337v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span><span class="category-tag">cs.DL</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    A quarter century ago, Wikipedia's decentralized, crowdsourced, and consensus-driven model replaced the centralized, expert-driven, and authority-based standard for encyclopedic knowledge curation. The emergence of generative AI encyclopedias, such as Grokipedia, possibly presents another potential shift in epistemic evolution. This study investigates whether AI- and human-curated encyclopedias rely on the same foundations of authority. We conducted a multi-scale comparative analysis of the citation networks from 72 matched article pairs, which cite a total of almost 60,000 sources. Using an 8-category epistemic classification, we mapped the "epistemic profiles" of the articles on each platform. Our findings reveal several quantitative and qualitative differences in how knowledge is sourced and encyclopedia claims are epistemologically justified. Grokipedia replaces Wikipedia's heavy reliance on peer-reviewed "Academic & Scholarly" work with a notable increase in "User-generated" and "Civic organization" sources. Comparative network analyses further show that Grokipedia employs very different epistemological profiles when sourcing leisure topics (such as Sports and Entertainment) and more societal sensitive civic topics (such as Politics & Conflicts, Geographical Entities, and General Knowledge & Society). Finally, we find a "scaling-law for AI-generated knowledge sourcing" that shows a linear relationship between article length and citation density, which is distinct from collective human reference sourcing. We conclude that this first implementation of an LLM-based encyclopedia does not merely automate knowledge production but restructures it. Given the notable changes and the important role of encyclopedias, we suggest the continuation and deepening of algorithm audits, such as the one presented here, in order to understand the ongoing epistemological shifts.
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            <a href="https://www.alphaxiv.org/abs/2512.03334v1" target="_blank" rel="noopener noreferrer">
                代码切换话语中的主题与社会语言变异建模：来自西班牙语-英语和西班牙语-瓜拉尼语的洞见
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        <div class="mb-2 text-base text-gray-700">
            Modeling Topics and Sociolinguistic Variation in Code-Switched Discourse: Insights from Spanish-English and Spanish-Guaraní
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nemika Tyagi, Nelvin Licona Guevara, Olga Kellert
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于代码切换（多语言混合）话语中的主题建模和社会语言变异分析，属于计算语言学和社会语言学的交叉领域。虽然涉及语言模型，但其核心研究问题（代码切换、社会语言变异）与推荐系统、搜索或广告的技术进步没有直接关联，也不属于LLM架构改进或异构数据统一建模的范畴。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 00:56:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03334v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03334v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    This study presents an LLM-assisted annotation pipeline for the sociolinguistic and topical analysis of bilingual discourse in two typologically distinct contexts: Spanish-English and Spanish-Guaraní. Using large language models, we automatically labeled topic, genre, and discourse-pragmatic functions across a total of 3,691 code-switched sentences, integrated demographic metadata from the Miami Bilingual Corpus, and enriched the Spanish-Guaraní dataset with new topic annotations. The resulting distributions reveal systematic links between gender, language dominance, and discourse function in the Miami data, and a clear diglossic division between formal Guaraní and informal Spanish in Paraguayan texts. These findings replicate and extend earlier interactional and sociolinguistic observations with corpus-scale quantitative evidence. The study demonstrates that large language models can reliably recover interpretable sociolinguistic patterns traditionally accessible only through manual annotation, advancing computational methods for cross-linguistic and low-resource bilingual research.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.04085v1" target="_blank" rel="noopener noreferrer">
                独特生活，共享世界：从单人生成视频中学习
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        <div class="mb-2 text-base text-gray-700">
            Unique Lives, Shared World: Learning from Single-Life Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tengda Han, Sayna Ebrahimi, Dilara Gokay, Li Yang Ku, Maks Ovsjanikov, Iva Babuk...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及从单人生成视频中学习，这属于计算机视觉领域，与推荐系统、搜索或广告的核心技术焦点无直接关联。标题未提及任何与推荐系统、搜索、广告、LLM技术、Transformer架构或异构数据处理相关的技术，因此与当前关注点高度不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04085v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04085v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce the "single-life" learning paradigm, where we train a distinct vision model exclusively on egocentric videos captured by one individual. We leverage the multiple viewpoints naturally captured within a single life to learn a visual encoder in a self-supervised manner. Our experiments demonstrate three key findings. First, models trained independently on different lives develop a highly aligned geometric understanding. We demonstrate this by training visual encoders on distinct datasets each capturing a different life, both indoors and outdoors, as well as introducing a novel cross-attention-based metric to quantify the functional alignment of the internal representations developed by different models. Second, we show that single-life models learn generalizable geometric representations that effectively transfer to downstream tasks, such as depth estimation, in unseen environments. Third, we demonstrate that training on up to 30 hours from one week of the same person's life leads to comparable performance to training on 30 hours of diverse web data, highlighting the strength of single-life representation learning. Overall, our results establish that the shared structure of the world, both leads to consistency in models trained on individual lives, and provides a powerful signal for visual representation learning.
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            <a href="https://www.alphaxiv.org/abs/2512.04082v1" target="_blank" rel="noopener noreferrer">
                PosterCopilot：面向专业平面设计的布局推理与可控编辑
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            PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiazhe Wei, Ken Li, Tianyu Lao, Haofan Wang, Liang Wang, Caifeng Shan, Chenyang ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于平面设计中的布局推理和可控编辑，属于AIGC（人工智能生成内容）和内容生成领域。根据用户列出的无关主题，明确排除了'Ads creative generation'（广告创意生成）和'AIGC, Content generation'等与推荐系统、搜索或广告排名无关的纯内容生成主题。因此，该论文与用户当前关注的推荐系统、搜索、广告核心进展、使能技术或直接应用均无直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:59:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04082v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04082v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Graphic design forms the cornerstone of modern visual communication, serving as a vital medium for promoting cultural and commercial events. Recent advances have explored automating this process using Large Multimodal Models (LMMs), yet existing methods often produce geometrically inaccurate layouts and lack the iterative, layer-specific editing required in professional workflows. To address these limitations, we present PosterCopilot, a framework that advances layout reasoning and controllable editing for professional graphic design. Specifically, we introduce a progressive three-stage training strategy that equips LMMs with geometric understanding and aesthetic reasoning for layout design, consisting of Perturbed Supervised Fine-Tuning, Reinforcement Learning for Visual-Reality Alignment, and Reinforcement Learning from Aesthetic Feedback. Furthermore, we develop a complete workflow that couples the trained LMM-based design model with generative models, enabling layer-controllable, iterative editing for precise element refinement while maintaining global visual consistency. Extensive experiments demonstrate that PosterCopilot achieves geometrically accurate and aesthetically superior layouts, offering unprecedented controllability for professional iterative design.
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            <a href="https://www.alphaxiv.org/abs/2512.04076v1" target="_blank" rel="noopener noreferrer">
                用于体素重建的辐射网格
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            Radiance Meshes for Volumetric Reconstruction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alexander Mai, Trevor Hedstrom, George Kopanas, Janne Kontkanen, Falko Kuester, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的体素重建和辐射场技术，属于3D视觉和图形学领域。根据用户指定的无关主题列表，明确排除了'Purely Vision、3D Vision、Graphic或Speech papers without clear relevance to RecSys/Search/Ads'，且该标题未显示与推荐系统、搜索或广告的直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:57:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04076v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04076v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.GR</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce radiance meshes, a technique for representing radiance fields with constant density tetrahedral cells produced with a Delaunay tetrahedralization. Unlike a Voronoi diagram, a Delaunay tetrahedralization yields simple triangles that are natively supported by existing hardware. As such, our model is able to perform exact and fast volume rendering using both rasterization and ray-tracing. We introduce a new rasterization method that achieves faster rendering speeds than all prior radiance field representations (assuming an equivalent number of primitives and resolution) across a variety of platforms. Optimizing the positions of Delaunay vertices introduces topological discontinuities (edge flips). To solve this, we use a Zip-NeRF-style backbone which allows us to express a smoothly varying field even when the topology changes. Our rendering method exactly evaluates the volume rendering equation and enables high quality, real-time view synthesis on standard consumer hardware. Our tetrahedral meshes also lend themselves to a variety of exciting applications including fisheye lens distortion, physics-based simulation, editing, and mesh extraction.
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            <a href="https://www.alphaxiv.org/abs/2512.04069v1" target="_blank" rel="noopener noreferrer">
                SpaceTools：通过双重交互式强化学习实现工具增强的空间推理
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            SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Siyi Chen, Mikaela Angelina Uy, Chan Hee Song, Faisal Ladhak, Adithyavairavan Mu...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及空间推理和工具增强的强化学习，这属于机器人学或具身智能领域，与推荐系统、搜索或广告的核心技术焦点无关。双重交互式强化学习可能涉及复杂的多智能体交互，但没有明确指向推荐、搜索或广告中的实际应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 18:50:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04069v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04069v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The agentic paradigm promises that VLMs can use a wide variety of tools that could augment these capabilities, such as depth estimators, segmentation models, and pose estimators. Yet it remains an open challenge how to realize this vision without solely relying on handcrafted prompting strategies or enforcing fixed, predefined tool pipelines that limit VLMs' ability to discover optimal tool-use patterns. Reinforcement Learning could overcome this gap, but has so far been limited to reasoning with a single visual tool due to the large search space in multi-tool reasoning. We introduce Double Interactive Reinforcement Learning (DIRL), a two-phase training framework where VLMs learn to coordinate multiple tools through interactive exploration and feedback. In the teaching phase, we combine demonstrations from a single tool specialist trained via interactive RL with traces from a frontier model using all tools. In the exploration phase, the model further refines multi-tool coordination through continued RL. Our model, SpaceTools, with tool-augmented spatial reasoning ability, achieves state-of-the-art performance on spatial understanding benchmarks (RoboSpatial-Home, BLINK, BOP-ASK) and demonstrates reliable real-world manipulation using a 7-DOF robot as a tool. DIRL provides substantial improvements over the vanilla SFT (+12% on RoboSpatial) and RL (+16% on RoboSpatial) baselines. Project page: https://spacetools.github.io/.
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            <a href="https://www.alphaxiv.org/abs/2512.04021v1" target="_blank" rel="noopener noreferrer">
                C3G：使用2K高斯函数学习紧凑的3D表示
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            C3G: Learning Compact 3D Representations with 2K Gaussians
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Honggyu An, Jaewoo Jung, Mungyeom Kim, Sunghwan Hong, Chaehyun Kim, Kazumi Fukud...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于3D表示学习，属于计算机视觉领域，与RecSys/Search/Ads的核心技术栈无直接关联。虽然压缩表示技术可能具有通用价值，但论文未提及任何文本、序列或推荐相关应用，因此不符合当前关注的任何技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:59:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04021v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04021v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Reconstructing and understanding 3D scenes from unposed sparse views in a feed-forward manner remains as a challenging task in 3D computer vision. Recent approaches use per-pixel 3D Gaussian Splatting for reconstruction, followed by a 2D-to-3D feature lifting stage for scene understanding. However, they generate excessive redundant Gaussians, causing high memory overhead and sub-optimal multi-view feature aggregation, leading to degraded novel view synthesis and scene understanding performance. We propose C3G, a novel feed-forward framework that estimates compact 3D Gaussians only at essential spatial locations, minimizing redundancy while enabling effective feature lifting. We introduce learnable tokens that aggregate multi-view features through self-attention to guide Gaussian generation, ensuring each Gaussian integrates relevant visual features across views. We then exploit the learned attention patterns for Gaussian decoding to efficiently lift features. Extensive experiments on pose-free novel view synthesis, 3D open-vocabulary segmentation, and view-invariant feature aggregation demonstrate our approach's effectiveness. Results show that a compact yet geometrically meaningful representation is sufficient for high-quality scene reconstruction and understanding, achieving superior memory efficiency and feature fidelity compared to existing methods.
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            <a href="https://www.alphaxiv.org/abs/2512.04019v1" target="_blank" rel="noopener noreferrer">
                超轻量级神经视频表示压缩
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Ultra-lightweight Neural Video Representation Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ho Man Kwan, Tianhao Peng, Ge Gao, Fan Zhang, Mike Nilsson, Andrew Gower, David ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频数据的神经表示压缩，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然压缩技术可能间接影响多媒体内容的存储或传输，但论文标题没有表明其在异构数据处理、Transformer架构或LLM应用方面的潜力，因此与当前关注点无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:56:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04019v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04019v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">eess.IV</span></div>
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                    Recent works have demonstrated the viability of utilizing over-fitted implicit neural representations (INRs) as alternatives to autoencoder-based models for neural video compression. Among these INR-based video codecs, Neural Video Representation Compression (NVRC) was the first to adopt a fully end-to-end compression framework that compresses INRs, achieving state-of-the-art performance. Moreover, some recently proposed lightweight INRs have shown comparable performance to their baseline codecs with computational complexity lower than 10kMACs/pixel. In this work, we extend NVRC toward lightweight representations, and propose NVRC-Lite, which incorporates two key changes. Firstly, we integrated multi-scale feature grids into our lightweight neural representation, and the use of higher resolution grids significantly improves the performance of INRs at low complexity. Secondly, we address the issue that existing INRs typically leverage autoregressive models for entropy coding: these are effective but impractical due to their slow coding speed. In this work, we propose an octree-based context model for entropy coding high-dimensional feature grids, which accelerates the entropy coding module of the model. Our experimental results demonstrate that NVRC-Lite outperforms C3, one of the best lightweight INR-based video codecs, with up to 21.03% and 23.06% BD-rate savings when measured in PSNR and MS-SSIM, respectively, while achieving 8.4x encoding and 2.5x decoding speedup. The implementation of NVRC-Lite will be made available.
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            <a href="https://www.alphaxiv.org/abs/2512.04015v1" target="_blank" rel="noopener noreferrer">
                在解耦的潜在图像表示中学习群作用
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Learning Group Actions In Disentangled Latent Image Representations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Farhana Hossain Swarnali, Miaomiao Zhang, Tonmoy Hossain
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于计算机视觉领域的表示学习，特别是图像潜在空间的群作用学习，这与RecSys/Search/Ads的核心技术或LLM/Transformer的使能技术没有直接关联。虽然解耦表示学习在理论上可能对多模态建模有启发，但该标题明确限定于图像数据，且未提及任何与推荐、搜索或广告相关的应用场景或技术迁移潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:52:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04015v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04015v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                    Modeling group actions on latent representations enables controllable transformations of high-dimensional image data. Prior works applying group-theoretic priors or modeling transformations typically operate in the high-dimensional data space, where group actions apply uniformly across the entire input, making it difficult to disentangle the subspace that varies under transformations. While latent-space methods offer greater flexibility, they still require manual partitioning of latent variables into equivariant and invariant subspaces, limiting the ability to robustly learn and operate group actions within the representation space. To address this, we introduce a novel end-to-end framework that for the first time learns group actions on latent image manifolds, automatically discovering transformation-relevant structures without manual intervention. Our method uses learnable binary masks with straight-through estimation to dynamically partition latent representations into transformation-sensitive and invariant components. We formulate this within a unified optimization framework that jointly learns latent disentanglement and group transformation mappings. The framework can be seamlessly integrated with any standard encoder-decoder architecture. We validate our approach on five 2D/3D image datasets, demonstrating its ability to automatically learn disentangled latent factors for group actions in diverse data, while downstream classification tasks confirm the effectiveness of the learned representations. Our code is publicly available at https://github.com/farhanaswarnali/Learning-Group-Actions-In-Disentangled-Latent-Image-Representations .
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            <a href="https://www.alphaxiv.org/abs/2512.04007v1" target="_blank" rel="noopener noreferrer">
                关于草图表示学习的时间性研究
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            On the Temporality for Sketch Representation Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marcelo Isaias de Moraes Junior, Moacir Antonelli Ponti
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于草图表示学习的时间特性，这属于计算机视觉中的特定子领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然表示学习是通用技术，但草图这一特定模态在RecSys/Search/Ads领域缺乏明确的应用场景，因此相关性极低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:46:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.04007v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.04007v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Sketches are simple human hand-drawn abstractions of complex scenes and real-world objects. Although the field of sketch representation learning has advanced significantly, there is still a gap in understanding the true relevance of the temporal aspect to the quality of these representations. This work investigates whether it is indeed justifiable to treat sketches as sequences, as well as which internal orders play a more relevant role. The results indicate that, although the use of traditional positional encodings is valid for modeling sketches as sequences, absolute coordinates consistently outperform relative ones. Furthermore, non-autoregressive decoders outperform their autoregressive counterparts. Finally, the importance of temporality was shown to depend on both the order considered and the task evaluated.
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            <a href="https://www.alphaxiv.org/abs/2512.03996v1" target="_blank" rel="noopener noreferrer">
                基于文本嵌入扰动的T2I扩散模型高效测试时缩放
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        <div class="mb-2 text-base text-gray-700">
            Highly Efficient Test-Time Scaling for T2I Diffusion Models with Text Embedding Perturbation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hang Xu, Linjiang Huang, Feng Zhao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文本到图像扩散模型的测试时效率优化，属于纯粹的AIGC/内容生成领域。虽然涉及扩散模型技术，但论文标题明确指向图像生成应用，与推荐系统、搜索或广告的排名、检索、个性化等核心任务没有直接关联，也不属于能够赋能这些领域的使能技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:27:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03996v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03996v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Test-time scaling (TTS) aims to achieve better results by increasing random sampling and evaluating samples based on rules and metrics. However, in text-to-image(T2I) diffusion models, most related works focus on search strategies and reward models, yet the impact of the stochastic characteristic of noise in T2I diffusion models on the method's performance remains unexplored. In this work, we analyze the effects of randomness in T2I diffusion models and explore a new format of randomness for TTS: text embedding perturbation, which couples with existing randomness like SDE-injected noise to enhance generative diversity and quality. We start with a frequency-domain analysis of these formats of randomness and their impact on generation, and find that these two randomness exhibit complementary behavior in the frequency domain: spatial noise favors low-frequency components (early steps), while text embedding perturbation enhances high-frequency details (later steps), thereby compensating for the potential limitations of spatial noise randomness in high-frequency manipulation. Concurrently, text embedding demonstrates varying levels of tolerance to perturbation across different dimensions of the generation process. Specifically, our method consists of two key designs: (1) Introducing step-based text embedding perturbation, combining frequency-guided noise schedules with spatial noise perturbation. (2) Adapting the perturbation intensity selectively based on their frequency-specific contributions to generation and tolerance to perturbation. Our approach can be seamlessly integrated into existing TTS methods and demonstrates significant improvements on multiple benchmarks with almost no additional computation. Code is available at \href{https://github.com/xuhang07/TEP-Diffusion}{https://github.com/xuhang07/TEP-Diffusion}.
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            <a href="https://www.alphaxiv.org/abs/2512.03995v1" target="_blank" rel="noopener noreferrer">
                人工微眼动补偿：为扑翼飞行器提供稳定视觉
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        <div class="mb-2 text-base text-gray-700">
            Artificial Microsaccade Compensation: Stable Vision for an Ornithopter
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Levi Burner, Guido de Croon, Yiannis Aloimonos
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文涉及计算机视觉和机器人技术，专注于扑翼飞行器的视觉稳定机制。它属于纯粹的视觉或机器人领域，与推荐系统、搜索或广告的核心进展、LLM技术、Transformer架构或异构数据统一建模没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:24:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03995v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03995v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Animals with foveated vision, including humans, experience microsaccades, small, rapid eye movements that they are not aware of. Inspired by this phenomenon, we develop a method for "Artificial Microsaccade Compensation". It can stabilize video captured by a tailless ornithopter that has resisted attempts to use camera-based sensing because it shakes at 12-20 Hz. Our approach minimizes changes in image intensity by optimizing over 3D rotation represented in SO(3). This results in a stabilized video, computed in real time, suitable for human viewing, and free from distortion. When adapted to hold a fixed viewing orientation, up to occasional saccades, it can dramatically reduce inter-frame motion while also benefiting from an efficient recursive update. When compared to Adobe Premier Pro's warp stabilizer, which is widely regarded as the best commercial video stabilization software available, our method achieves higher quality results while also running in real time.
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            <a href="https://www.alphaxiv.org/abs/2512.03992v1" target="_blank" rel="noopener noreferrer">
                DIQ-H：评估视觉语言模型在时序视觉退化下的幻觉持续性
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            DIQ-H: Evaluating Hallucination Persistence in VLMs Under Temporal Visual Degradation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zexin Lin, Hawen Wan, Yebin Zhong, Xiaoqiang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视觉语言模型（VLM）的幻觉评估，这属于纯粹的VLM/NLP评估基准研究，与推荐系统、搜索或广告的核心技术进展无关。虽然提到了视觉退化，但核心是评估幻觉持续性，属于用户明确排除的“幻觉、评估基准或其他纯NLP中心主题”范畴。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:22:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03992v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03992v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision-Language Models (VLMs) deployed in safety-critical applications such as autonomous driving must handle continuous visual streams under imperfect conditions. However, existing benchmarks focus on static, high-quality images and ignore temporal degradation and error propagation, which are critical failure modes where transient visual corruption induces hallucinations that persist across subsequent frames. We introduce DIQ-H, the first benchmark for evaluating VLM robustness under dynamic visual degradation in temporal sequences. DIQ-H applies physics-based corruptions including motion blur, sensor noise, and compression artifacts, and measures hallucination persistence, error recovery, and temporal consistency through multi-turn question-answering tasks. To enable scalable annotation, we propose Uncertainty-Guided Iterative Refinement (UIR), which generates reliable pseudo-ground-truth using lightweight VLMs with uncertainty filtering, achieving a 15.3 percent accuracy improvement. Experiments on 16 state-of-the-art VLMs reveal substantial robustness gaps: even advanced models such as GPT-4o achieve only a 78.5 percent recovery rate, while open-source models struggle with temporal consistency at less than 60 percent. DIQ-H provides a comprehensive platform for evaluating VLM reliability in real-world deployments.
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            <a href="https://www.alphaxiv.org/abs/2512.03981v1" target="_blank" rel="noopener noreferrer">
                DirectDrag：通过读出引导特征对齐实现高保真、无掩码、无提示的基于拖拽的图像编辑
            </a>
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            DirectDrag: High-Fidelity, Mask-Free, Prompt-Free Drag-based Image Editing via Readout-Guided Feature Alignment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sheng-Hao Liao, Shang-Fu Chen, Tai-Ming Huang, Wen-Huang Cheng, Kai-Lung Hua
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像编辑技术，属于纯粹的视觉领域研究。虽然标题提到“特征对齐”，但这与推荐系统、搜索或广告中的异构数据统一建模没有直接关联。该技术没有展示在推荐、搜索或广告排名任务中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:12:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03981v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03981v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Drag-based image editing using generative models provides intuitive control over image structures. However, existing methods rely heavily on manually provided masks and textual prompts to preserve semantic fidelity and motion precision. Removing these constraints creates a fundamental trade-off: visual artifacts without masks and poor spatial control without prompts. To address these limitations, we propose DirectDrag, a novel mask- and prompt-free editing framework. DirectDrag enables precise and efficient manipulation with minimal user input while maintaining high image fidelity and accurate point alignment. DirectDrag introduces two key innovations. First, we design an Auto Soft Mask Generation module that intelligently infers editable regions from point displacement, automatically localizing deformation along movement paths while preserving contextual integrity through the generative model's inherent capacity. Second, we develop a Readout-Guided Feature Alignment mechanism that leverages intermediate diffusion activations to maintain structural consistency during point-based edits, substantially improving visual fidelity. Despite operating without manual mask or prompt, DirectDrag achieves superior image quality compared to existing methods while maintaining competitive drag accuracy. Extensive experiments on DragBench and real-world scenarios demonstrate the effectiveness and practicality of DirectDrag for high-quality, interactive image manipulation. Project Page: https://frakw.github.io/DirectDrag/. Code is available at: https://github.com/frakw/DirectDrag.
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            <a href="https://www.alphaxiv.org/abs/2512.03979v1" target="_blank" rel="noopener noreferrer">
                BlurDM：一种用于图像去模糊的模糊扩散模型
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            BlurDM: A Blur Diffusion Model for Image Deblurring
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jin-Ting He, Fu-Jen Tsai, Yan-Tsung Peng, Min-Hung Chen, Chia-Wen Lin, Yen-Yu Li...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像去模糊任务，属于纯粹的视觉处理领域。虽然扩散模型是生成式AI的重要技术，但该论文没有展示与推荐系统、搜索或广告领域的直接关联或潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 17:10:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03979v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03979v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diffusion models show promise for dynamic scene deblurring; however, existing studies often fail to leverage the intrinsic nature of the blurring process within diffusion models, limiting their full potential. To address it, we present a Blur Diffusion Model (BlurDM), which seamlessly integrates the blur formation process into diffusion for image deblurring. Observing that motion blur stems from continuous exposure, BlurDM implicitly models the blur formation process through a dual-diffusion forward scheme, diffusing both noise and blur onto a sharp image. During the reverse generation process, we derive a dual denoising and deblurring formulation, enabling BlurDM to recover the sharp image by simultaneously denoising and deblurring, given pure Gaussian noise conditioned on the blurred image as input. Additionally, to efficiently integrate BlurDM into deblurring networks, we perform BlurDM in the latent space, forming a flexible prior generation network for deblurring. Extensive experiments demonstrate that BlurDM significantly and consistently enhances existing deblurring methods on four benchmark datasets. The source code is available at https://github.com/Jin-Ting-He/BlurDM.
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            <a href="https://www.alphaxiv.org/abs/2512.03964v1" target="_blank" rel="noopener noreferrer">
                为身份训练，为可控性推理：一种无需调优的面部个性化统一方法
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            Training for Identity, Inference for Controllability: A Unified Approach to Tuning-Free Face Personalization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lianyu Pang, Ji Zhou, Qiping Wang, Baoquan Zhao, Zhenguo Yang, Qing Li, Xudong M...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉领域的面部个性化技术，属于纯粹的视觉应用，与推荐系统、搜索或广告的核心技术没有直接关联。标题中提到的'身份训练'和'可控性推理'是计算机视觉中的特定概念，无法合理推断出在推荐/搜索/广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 16:57:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03964v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03964v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Tuning-free face personalization methods have developed along two distinct paradigms: text embedding approaches that map facial features into the text embedding space, and adapter-based methods that inject features through auxiliary cross-attention layers. While both paradigms have shown promise, existing methods struggle to simultaneously achieve high identity fidelity and flexible text controllability. We introduce UniID, a unified tuning-free framework that synergistically integrates both paradigms. Our key insight is that when merging these approaches, they should mutually reinforce only identity-relevant information while preserving the original diffusion prior for non-identity attributes. We realize this through a principled training-inference strategy: during training, we employ an identity-focused learning scheme that guides both branches to capture identity features exclusively; at inference, we introduce a normalized rescaling mechanism that recovers the text controllability of the base diffusion model while enabling complementary identity signals to enhance each other. This principled design enables UniID to achieve high-fidelity face personalization with flexible text controllability. Extensive experiments against six state-of-the-art methods demonstrate that UniID achieves superior performance in both identity preservation and text controllability. Code will be available at https://github.com/lyuPang/UniID
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            <a href="https://www.alphaxiv.org/abs/2512.03962v1" target="_blank" rel="noopener noreferrer">
                Tada-DIP：输入自适应深度图像先验用于单次3D图像重建
            </a>
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            Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Evan Bell, Shijun Liang, Ismail Alkhouri, Saiprasad Ravishankar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D图像重建的计算机视觉任务，属于纯粹的视觉领域研究。虽然涉及深度学习技术，但标题中没有任何元素表明与推荐系统、搜索或广告有直接或潜在的应用关联。根据用户指定的不相关主题，这属于'纯粹的视觉论文，没有明确的推荐/搜索/广告相关性'。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 16:56:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03962v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03962v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Deep Image Prior (DIP) has recently emerged as a promising one-shot neural-network based image reconstruction method. However, DIP has seen limited application to 3D image reconstruction problems. In this work, we introduce Tada-DIP, a highly effective and fully 3D DIP method for solving 3D inverse problems. By combining input-adaptation and denoising regularization, Tada-DIP produces high-quality 3D reconstructions while avoiding the overfitting phenomenon that is common in DIP. Experiments on sparse-view X-ray computed tomography reconstruction validate the effectiveness of the proposed method, demonstrating that Tada-DIP produces much better reconstructions than training-data-free baselines and achieves reconstruction performance on par with a supervised network trained using a large dataset with fully-sampled volumes.
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            <a href="https://www.alphaxiv.org/abs/2512.03939v1" target="_blank" rel="noopener noreferrer">
                MUT3R：面向动态三维重建的运动感知更新Transformer
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            MUT3R: Motion-aware Updating Transformer for Dynamic 3D Reconstruction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guole Shen, Tianchen Deng, Xingrui Qin, Nailin Wang, Jianyu Wang, Yanbo Wang, Yo...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于动态三维重建，属于计算机视觉和3D视觉领域，与推荐系统、搜索或广告的核心技术焦点无直接关联。标题中提到的Transformer架构虽然是相关技术，但该研究主要应用于3D视觉任务，没有明确指向或潜在应用于推荐、搜索或广告领域的迹象。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 16:36:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03939v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03939v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Recent stateful recurrent neural networks have achieved remarkable progress on static 3D reconstruction but remain vulnerable to motion-induced artifacts, where non-rigid regions corrupt attention propagation between the spatial memory and image feature. By analyzing the internal behaviors of the state and image token updating mechanism, we find that aggregating self-attention maps across layers reveals a consistent pattern: dynamic regions are naturally down-weighted, exposing an implicit motion cue that the pretrained transformer already encodes but never explicitly uses. Motivated by this observation, we introduce MUT3R, a training-free framework that applies the attention-derived motion cue to suppress dynamic content in the early layers of the transformer during inference. Our attention-level gating module suppresses the influence of dynamic regions before their artifacts propagate through the feature hierarchy. Notably, we do not retrain or fine-tune the model; we let the pretrained transformer diagnose its own motion cues and correct itself. This early regulation stabilizes geometric reasoning in streaming scenarios and leads to improvements in temporal consistency and camera pose robustness across multiple dynamic benchmarks, offering a simple and training-free pathway toward motion-aware streaming reconstruction.
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                超越真实标注：图像复原的增强监督方法
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            Beyond the Ground Truth: Enhanced Supervision for Image Restoration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Donghun Ryou, Inju Ha, Sanghyeok Chu, Bohyung Han
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于计算机视觉领域的图像复原任务，属于纯粹的视觉处理范畴。虽然涉及监督学习技术，但缺乏与推荐系统、搜索或广告领域相关的技术元素或应用潜力，完全符合被排除的'Purely Vision'类别。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 16:30:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03932v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03932v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Deep learning-based image restoration has achieved significant success. However, when addressing real-world degradations, model performance is limited by the quality of ground-truth images in datasets due to practical constraints in data acquisition. To address this limitation, we propose a novel framework that enhances existing ground truth images to provide higher-quality supervision for real-world restoration. Our framework generates perceptually enhanced ground truth images using super-resolution by incorporating adaptive frequency masks, which are learned by a conditional frequency mask generator. These masks guide the optimal fusion of frequency components from the original ground truth and its super-resolved variants, yielding enhanced ground truth images. This frequency-domain mixup preserves the semantic consistency of the original content while selectively enriching perceptual details, preventing hallucinated artifacts that could compromise fidelity. The enhanced ground truth images are used to train a lightweight output refinement network that can be seamlessly integrated with existing restoration models. Extensive experiments demonstrate that our approach consistently improves the quality of restored images. We further validate the effectiveness of both supervision enhancement and output refinement through user studies. Code is available at https://github.com/dhryougit/Beyond-the-Ground-Truth.
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            <a href="https://www.alphaxiv.org/abs/2512.03883v1" target="_blank" rel="noopener noreferrer">
                用于观察等待内窥镜中直肠肿瘤再生评估的双交叉注意力孪生Transformer
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            Dual Cross-Attention Siamese Transformer for Rectal Tumor Regrowth Assessment in Watch-and-Wait Endoscopy
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jorge Tapias Gomez, Despoina Kanata, Aneesh Rangnekar, Christina Lee, Julio Garc...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学领域（直肠肿瘤评估），属于明确的无关主题。虽然使用了Transformer架构，但应用场景是医疗诊断，与推荐系统、搜索或广告领域无直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 15:34:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03883v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03883v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Increasing evidence supports watch-and-wait (WW) surveillance for patients with rectal cancer who show clinical complete response (cCR) at restaging following total neoadjuvant treatment (TNT). However, objectively accurate methods to early detect local regrowth (LR) from follow-up endoscopy images during WW are essential to manage care and prevent distant metastases. Hence, we developed a Siamese Swin Transformer with Dual Cross-Attention (SSDCA) to combine longitudinal endoscopic images at restaging and follow-up and distinguish cCR from LR. SSDCA leverages pretrained Swin transformers to extract domain agnostic features and enhance robustness to imaging variations. Dual cross attention is implemented to emphasize features from the two scans without requiring any spatial alignment of images to predict response. SSDCA as well as Swin-based baselines were trained using image pairs from 135 patients and evaluated on a held-out set of image pairs from 62 patients. SSDCA produced the best balanced accuracy (81.76\% $\pm$ 0.04), sensitivity (90.07\% $\pm$ 0.08), and specificity (72.86\% $\pm$ 0.05). Robustness analysis showed stable performance irrespective of artifacts including blood, stool, telangiectasia, and poor image quality. UMAP clustering of extracted features showed maximal inter-cluster separation (1.45 $\pm$ 0.18) and minimal intra-cluster dispersion (1.07 $\pm$ 0.19) with SSDCA, confirming discriminative representation learning.
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            <a href="https://www.alphaxiv.org/abs/2512.03869v1" target="_blank" rel="noopener noreferrer">
                面向大规模图基脑血管分析的自适应框架
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            An Automated Framework for Large-Scale Graph-Based Cerebrovascular Analysis
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniele Falcetta, Liane S. Canas, Lorenzo Suppa, Matteo Pentassuglia, Jon Cleary...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学/生物学领域的脑血管分析，属于明确的无关主题。虽然涉及图结构和大规模分析，但核心应用领域（脑血管分析）与推荐系统、搜索或广告完全无关，且没有迹象表明该技术可迁移至相关领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 15:21:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03869v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03869v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CY</span></div>
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                    We present CaravelMetrics, a computational framework for automated cerebrovascular analysis that models vessel morphology through skeletonization-derived graph representations. The framework integrates atlas-based regional parcellation, centerline extraction, and graph construction to compute fifteen morphometric, topological, fractal, and geometric features. The features can be estimated globally from the complete vascular network or regionally within arterial territories, enabling multiscale characterization of cerebrovascular organization. Applied to 570 3D TOF-MRA scans from the IXI dataset (ages 20-86), CaravelMetrics yields reproducible vessel graphs capturing age- and sex-related variations and education-associated increases in vascular complexity, consistent with findings reported in the literature. The framework provides a scalable and fully automated approach for quantitative cerebrovascular feature extraction, supporting normative modeling and population-level studies of vascular health and aging.
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            <a href="https://www.alphaxiv.org/abs/2512.03854v1" target="_blank" rel="noopener noreferrer">
                来自中东代表性不足人群的前列腺活检全切片图像数据集
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            Prostate biopsy whole slide image dataset from an underrepresented Middle Eastern population
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Peshawa J. Muhammad Ali, Navin Vincent, Saman S. Abdulla, Han N. Mohammed Fadhl,...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及医学领域（前列腺活检）和特定数据集，这属于明确的无关主题（医学/生物学领域特定应用）。该论文与推荐系统、搜索、广告、LLM技术或Transformer架构没有任何关联，也没有展示出在相关领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:54:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03854v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03854v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Artificial intelligence (AI) is increasingly used in digital pathology. Publicly available histopathology datasets remain scarce, and those that do exist predominantly represent Western populations. Consequently, the generalizability of AI models to populations from less digitized regions, such as the Middle East, is largely unknown. This motivates the public release of our dataset to support the development and validation of pathology AI models across globally diverse populations. We present 339 whole-slide images of prostate core needle biopsies from a consecutive series of 185 patients collected in Erbil, Iraq. The slides are associated with Gleason scores and International Society of Urological Pathology grades assigned independently by three pathologists. Scanning was performed using two high-throughput scanners (Leica and Hamamatsu) and one compact scanner (Grundium). All slides were de-identified and are provided in their native formats without further conversion. The dataset enables grading concordance analyses, color normalization, and cross-scanner robustness evaluations. Data will be deposited in the Bioimage Archive (BIA) under accession code: to be announced (TBA), and released under a CC BY 4.0 license.
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            <a href="https://www.alphaxiv.org/abs/2512.03852v1" target="_blank" rel="noopener noreferrer">
                基于频率感知Mamba的恶劣天气下交通图像恢复
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            Traffic Image Restoration under Adverse Weather via Frequency-Aware Mamba
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Liwen Pan, Longguang Wang, Guangwei Gao, Jun Wang, Jun Shi, Juncheng Li
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及交通图像恢复和恶劣天气条件，这属于计算机视觉领域，与推荐系统、搜索或广告的核心技术焦点无关。虽然Mamba是一种序列建模架构，但论文的应用场景（交通图像恢复）与指定的技术领域没有直接关联，也没有表明其方法在推荐、搜索或广告中有潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:50:20
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                <a href="https://arxiv.org/abs/2512.03852v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03852v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Traffic image restoration under adverse weather conditions remains a critical challenge for intelligent transportation systems. Existing methods primarily focus on spatial-domain modeling but neglect frequency-domain priors. Although the emerging Mamba architecture excels at long-range dependency modeling through patch-wise correlation analysis, its potential for frequency-domain feature extraction remains unexplored. To address this, we propose Frequency-Aware Mamba (FAMamba), a novel framework that integrates frequency guidance with sequence modeling for efficient image restoration. Our architecture consists of two key components: (1) a Dual-Branch Feature Extraction Block (DFEB) that enhances local-global interaction via bidirectional 2D frequency-adaptive scanning, dynamically adjusting traversal paths based on sub-band texture distributions; and (2) a Prior-Guided Block (PGB) that refines texture details through wavelet-based high-frequency residual learning, enabling high-quality image reconstruction with precise details. Meanwhile, we design a novel Adaptive Frequency Scanning Mechanism (AFSM) for the Mamba architecture, which enables the Mamba to achieve frequency-domain scanning across distinct subgraphs, thereby fully leveraging the texture distribution characteristics inherent in subgraph structures. Extensive experiments demonstrate the efficiency and effectiveness of FAMamba.
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            <a href="https://www.alphaxiv.org/abs/2512.03848v1" target="_blank" rel="noopener noreferrer">
                PULSE：一种用于心脏分割、诊断和少样本跨模态临床适应的统一多任务架构
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            PULSE: A Unified Multi-Task Architecture for Cardiac Segmentation, Diagnosis, and Few-Shot Cross-Modality Clinical Adaptation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hania Ghouse, Maryam Alsharqi, Farhad R. Nezami, Muzammil Behzad
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域（心脏分割、诊断、临床适应），属于明确的医学应用范畴，与搜索、推荐、广告等商业系统无关。虽然涉及多任务架构和跨模态适应等技术概念，但核心应用场景（医疗影像分析）完全在指定的不相关主题范围内，没有任何潜在的应用于推荐系统、搜索或广告的可能性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:49:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03848v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03848v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Cardiac image analysis remains fragmented across tasks: anatomical segmentation, disease classification, and grounded clinical report generation are typically handled by separate networks trained under different data regimes. No existing framework unifies these objectives within a single architecture while retaining generalization across imaging modalities and datasets. We introduce PULSE, a multi-task vision-language framework built on self-supervised representations and optimized through a composite supervision strategy that balances region overlap learning, pixel wise classification fidelity, and boundary aware IoU refinement. A multi-scale token reconstruction decoder enables anatomical segmentation, while shared global representations support disease classification and clinically grounded text output allowing the model to transition from pixels to structures and finally clinical reasoning within one architecture. Unlike prior task-specific pipelines, PULSE learns task-invariant cardiac priors, generalizes robustly across datasets, and can be adapted to new imaging modalities with minimal supervision. This moves the field closer to a scalable, foundation style cardiac analysis framework.
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                CoDA：从文本到图像扩散模型到免训练数据集蒸馏
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        <div class="mb-2 text-base text-gray-700">
            CoDA: From Text-to-Image Diffusion Models to Training-Free Dataset Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Letian Zhou, Songhua Liu, Xinchao Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及文本到图像扩散模型和数据集蒸馏，这属于纯粹的视觉和生成式AI领域。虽然扩散模型是LLM相关技术，但该论文专注于图像生成和数据集压缩，与推荐系统、搜索或广告的核心技术没有直接关联，也不符合您关注的LLM在推荐/搜索/广告中的直接应用或异构数据统一建模等方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:45:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03844v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03844v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Prevailing Dataset Distillation (DD) methods leveraging generative models confront two fundamental limitations. First, despite pioneering the use of diffusion models in DD and delivering impressive performance, the vast majority of approaches paradoxically require a diffusion model pre-trained on the full target dataset, undermining the very purpose of DD and incurring prohibitive training costs. Second, although some methods turn to general text-to-image models without relying on such target-specific training, they suffer from a significant distributional mismatch, as the web-scale priors encapsulated in these foundation models fail to faithfully capture the target-specific semantics, leading to suboptimal performance. To tackle these challenges, we propose Core Distribution Alignment (CoDA), a framework that enables effective DD using only an off-the-shelf text-to-image model. Our key idea is to first identify the "intrinsic core distribution" of the target dataset using a robust density-based discovery mechanism. We then steer the generative process to align the generated samples with this core distribution. By doing so, CoDA effectively bridges the gap between general-purpose generative priors and target semantics, yielding highly representative distilled datasets. Extensive experiments suggest that, without relying on a generative model specifically trained on the target dataset, CoDA achieves performance on par with or even superior to previous methods with such reliance across all benchmarks, including ImageNet-1K and its subsets. Notably, it establishes a new state-of-the-art accuracy of 60.4% at the 50-images-per-class (IPC) setup on ImageNet-1K. Our code is available on the project webpage: https://github.com/zzzlt422/CoDA
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            <a href="https://www.alphaxiv.org/abs/2512.03837v1" target="_blank" rel="noopener noreferrer">
                用于RGB视频动作识别的热图池化网络
            </a>
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            Heatmap Pooling Network for Action Recognition from RGB Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mengyuan Liu, Jinfu Liu, Yongkang Jiang, Bin He
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于纯视觉领域的动作识别，属于计算机视觉中的特定任务。虽然动作识别可能在某些边缘场景中与推荐系统相关（如视频内容理解），但论文标题明确限定于RGB视频处理，没有涉及推荐、搜索或广告的核心技术，也不包含LLM、Transformer架构或多模态建模等与当前关注点相关的元素。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:36:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03837v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03837v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Human action recognition (HAR) in videos has garnered widespread attention due to the rich information in RGB videos. Nevertheless, existing methods for extracting deep features from RGB videos face challenges such as information redundancy, susceptibility to noise and high storage costs. To address these issues and fully harness the useful information in videos, we propose a novel heatmap pooling network (HP-Net) for action recognition from videos, which extracts information-rich, robust and concise pooled features of the human body in videos through a feedback pooling module. The extracted pooled features demonstrate obvious performance advantages over the previously obtained pose data and heatmap features from videos. In addition, we design a spatial-motion co-learning module and a text refinement modulation module to integrate the extracted pooled features with other multimodal data, enabling more robust action recognition. Extensive experiments on several benchmarks namely NTU RGB+D 60, NTU RGB+D 120, Toyota-Smarthome and UAV-Human consistently verify the effectiveness of our HP-Net, which outperforms the existing human action recognition methods. Our code is publicly available at: https://github.com/liujf69/HPNet-Action.
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            <a href="https://www.alphaxiv.org/abs/2512.03834v1" target="_blank" rel="noopener noreferrer">
                Lean Unet：用于图像分割的紧凑模型
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Lean Unet: A Compact Model for Image Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ture Hassler, Ida Åkerholm, Marcus Nordström, Gabriele Balletti, Orcun Goksel
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像分割任务，属于纯粹的视觉领域研究。虽然提到了模型压缩（紧凑模型），但论文标题没有表明与推荐系统、搜索或广告有任何直接关联，也没有展示将视觉技术应用于异构数据处理（如VLM类比）的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:35:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03834v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03834v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Unet and its variations have been standard in semantic image segmentation, especially for computer assisted radiology. Current Unet architectures iteratively downsample spatial resolution while increasing channel dimensions to preserve information content. Such a structure demands a large memory footprint, limiting training batch sizes and increasing inference latency. Channel pruning compresses Unet architecture without accuracy loss, but requires lengthy optimization and may not generalize across tasks and datasets. By investigating Unet pruning, we hypothesize that the final structure is the crucial factor, not the channel selection strategy of pruning. Based on our observations, we propose a lean Unet architecture (LUnet) with a compact, flat hierarchy where channels are not doubled as resolution is halved. We evaluate on a public MRI dataset allowing comparable reporting, as well as on two internal CT datasets. We show that a state-of-the-art pruning solution (STAMP) mainly prunes from the layers with the highest number of channels. Comparatively, simply eliminating a random channel at the pruning-identified layer or at the largest layer achieves similar or better performance. Our proposed LUnet with fixed architectures and over 30 times fewer parameters achieves performance comparable to both conventional Unet counterparts and data-adaptively pruned networks. The proposed lean Unet with constant channel count across layers requires far fewer parameters while achieving performance superior to standard Unet for the same total number of parameters. Skip connections allow Unet bottleneck channels to be largely reduced, unlike standard encoder-decoder architectures requiring increased bottleneck channels for information propagation.
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            <a href="https://www.alphaxiv.org/abs/2512.03827v1" target="_blank" rel="noopener noreferrer">
                一种基于摄像头的稳健呼吸频率测量方法
            </a>
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        <div class="mb-2 text-base text-gray-700">
            A Robust Camera-based Method for Breath Rate Measurement
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alexey Protopopov
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向计算机视觉在生物医学测量领域的应用（呼吸频率测量），这属于明确的无关主题（医学/生物医学应用）。该技术没有显示出与推荐系统、搜索或广告领域的任何潜在应用关联，完全超出了当前关注范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:19:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03827v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03827v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Proliferation of cheap and accessible cameras makes it possible to measure a subject's breath rate from video footage alone. Recent works on this topic have proposed a variety of approaches for accurately measuring human breath rate, however they are either tested in near-ideal conditions, or produce results that are not sufficiently accurate. The present study proposes a more robust method to measure breath rate in humans with minimal hardware requirements using a combination of mathematical transforms with a relative deviation from the ground truth of less than 5%. The method was tested on videos taken from 14 volunteers with a total duration of over 2 hours 30 minutes. The obtained results were compared to reference data and the average mean absolute error was found to be at 0.57 respirations per minute, which is noticeably better than the results from previous works. The breath rate measurement method proposed in the present article is more resistant to distortions caused by subject movement and thus allows one to remotely measure the subject's breath rate without any significant limitations on the subject's behavior.
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            <a href="https://www.alphaxiv.org/abs/2512.03817v1" target="_blank" rel="noopener noreferrer">
                HieroGlyphTranslator：埃及象形文字自动识别与英译
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            HieroGlyphTranslator: Automatic Recognition and Translation of Egyptian Hieroglyphs to English
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ahmed Nasser, Marwan Mohamed, Alaa Sherif, Basmala Mahmoud, Shereen Yehia, Asmaa...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于古埃及象形文字的识别与翻译，属于特定领域的历史语言处理任务。它不涉及推荐系统、搜索或广告的核心进展，也不包含LLM、Transformer架构或异构数据统一建模等与当前关注点相关的技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 14:05:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03817v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03817v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Egyptian hieroglyphs, the ancient Egyptian writing system, are composed entirely of drawings. Translating these glyphs into English poses various challenges, including the fact that a single glyph can have multiple meanings. Deep learning translation applications are evolving rapidly, producing remarkable results that significantly impact our lives. In this research, we propose a method for the automatic recognition and translation of ancient Egyptian hieroglyphs from images to English. This study utilized two datasets for classification and translation: the Morris Franken dataset and the EgyptianTranslation dataset. Our approach is divided into three stages: segmentation (using Contour and Detectron2), mapping symbols to Gardiner codes, and translation (using the CNN model). The model achieved a BLEU score of 42.2, a significant result compared to previous research.
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            <a href="https://www.alphaxiv.org/abs/2512.03751v1" target="_blank" rel="noopener noreferrer">
                基于改进ResNet34网络的脑肿瘤分类方法研究
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            Research on Brain Tumor Classification Method Based on Improved ResNet34 Network
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yufeng Li, Wenchao Zhao, Bo Dang, Weimin Wang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">This paper focuses on medical image classification (brain tumors) using a computer vision architecture (ResNet34), which falls under 'Medical, Biology, Chemistry, Physics or other domain-specific applications' in the irrelevant topics list. It has no clear connection to RecSys, Search, or Ads domains, and the technical approach (improved ResNet34) doesn't represent enabling LLM/Transformer technology with potential applications in these areas.</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 12:47:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03751v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03751v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Previously, image interpretation in radiology relied heavily on manual methods. However, manual classification of brain tumor medical images is time-consuming and labor-intensive. Even with shallow convolutional neural network models, the accuracy is not ideal. To improve the efficiency and accuracy of brain tumor image classification, this paper proposes a brain tumor classification model based on an improved ResNet34 network. This model uses the ResNet34 residual network as the backbone network and incorporates multi-scale feature extraction. It uses a multi-scale input module as the first layer of the ResNet34 network and an Inception v2 module as the residual downsampling layer. Furthermore, a channel attention mechanism module assigns different weights to different channels of the image from a channel domain perspective, obtaining more important feature information. The results after a five-fold crossover experiment show that the average classification accuracy of the improved network model is approximately 98.8%, which is not only 1% higher than ResNet34, but also only 80% of the number of parameters of the original model. Therefore, the improved network model not only improves accuracy but also reduces clutter, achieving a classification effect with fewer parameters and higher accuracy.
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            <a href="https://www.alphaxiv.org/abs/2512.03749v1" target="_blank" rel="noopener noreferrer">
                文本到图像扩散模型的完全无监督自去偏
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            Fully Unsupervised Self-debiasing of Text-to-Image Diffusion Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Korada Sri Vardhana, Shrikrishna Lolla, Soma Biswas
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文本到图像生成中的去偏问题，属于纯粹的AIGC/内容生成领域，与推荐系统、搜索或广告的排名任务无关。虽然扩散模型是生成模型的一种，但论文没有涉及任何与推荐、搜索或广告相关的应用或潜在应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 12:46:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03749v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03749v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Text-to-image (T2I) diffusion models have achieved widespread success due to their ability to generate high-resolution, photorealistic images. These models are trained on large-scale datasets, like LAION-5B, often scraped from the internet. However, since this data contains numerous biases, the models inherently learn and reproduce them, resulting in stereotypical outputs. We introduce SelfDebias, a fully unsupervised test-time debiasing method applicable to any diffusion model that uses a UNet as its noise predictor. SelfDebias identifies semantic clusters in an image encoder's embedding space and uses these clusters to guide the diffusion process during inference, minimizing the KL divergence between the output distribution and the uniform distribution. Unlike supervised approaches, SelfDebias does not require human-annotated datasets or external classifiers trained for each generated concept. Instead, it is designed to automatically identify semantic modes. Extensive experiments show that SelfDebias generalizes across prompts and diffusion model architectures, including both conditional and unconditional models. It not only effectively debiases images along key demographic dimensions while maintaining the visual fidelity of the generated images, but also more abstract concepts for which identifying biases is also challenging.
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            <a href="https://www.alphaxiv.org/abs/2512.03745v1" target="_blank" rel="noopener noreferrer">
                用于无监督可见光-红外行人重识别的双层级模态去偏学习
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiaze Li, Yan Lu, Bin Liu, Guojun Yin, Mang Ye
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的行人重识别任务，涉及可见光和红外两种视觉模态。虽然标题提到'模态'和'去偏'，但这是纯粹的视觉领域研究，与推荐系统、搜索或广告没有直接关联。该工作没有展示任何在异构数据统一建模方面的潜在应用，也不属于LLM、Transformer架构或推荐系统核心进展的范畴。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 12:43:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03745v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03745v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Two-stage learning pipeline has achieved promising results in unsupervised visible-infrared person re-identification (USL-VI-ReID). It first performs single-modality learning and then operates cross-modality learning to tackle the modality discrepancy. Although promising, this pipeline inevitably introduces modality bias: modality-specific cues learned in the single-modality training naturally propagate into the following cross-modality learning, impairing identity discrimination and generalization. To address this issue, we propose a Dual-level Modality Debiasing Learning (DMDL) framework that implements debiasing at both the model and optimization levels. At the model level, we propose a Causality-inspired Adjustment Intervention (CAI) module that replaces likelihood-based modeling with causal modeling, preventing modality-induced spurious patterns from being introduced, leading to a low-biased model. At the optimization level, a Collaborative Bias-free Training (CBT) strategy is introduced to interrupt the propagation of modality bias across data, labels, and features by integrating modality-specific augmentation, label refinement, and feature alignment. Extensive experiments on benchmark datasets demonstrate that DMDL could enable modality-invariant feature learning and a more generalized model.
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            <a href="https://www.alphaxiv.org/abs/2512.03730v1" target="_blank" rel="noopener noreferrer">
                开箱即用：针对目标检测器的黑盒因果攻击
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            Out-of-the-box: Black-box Causal Attacks on Object Detectors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Melane Navaratnarajah, David A. Kelly, Hana Chockler
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉领域的目标检测器安全攻击，属于对抗性攻击范畴。虽然提到了“黑盒”和“因果”等概念，但核心内容明显属于计算机视觉安全领域，与推荐系统、搜索、广告等核心业务领域以及LLM/Transformer技术进展无直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 12:17:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03730v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03730v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Adversarial perturbations are a useful way to expose vulnerabilities in object detectors. Existing perturbation methods are frequently white-box and architecture specific. More importantly, while they are often successful, it is rarely clear why they work. Insights into the mechanism of this success would allow developers to understand and analyze these attacks, as well as fine-tune the model to prevent them. This paper presents BlackCAtt, a black-box algorithm and a tool, which uses minimal, causally sufficient pixel sets to construct explainable, imperceptible, reproducible, architecture-agnostic attacks on object detectors. BlackCAtt combines causal pixels with bounding boxes produced by object detectors to create adversarial attacks that lead to the loss, modification or addition of a bounding box. BlackCAtt works across different object detectors of different sizes and architectures, treating the detector as a black box. We compare the performance of BlackCAtt with other black-box attack methods and show that identification of causal pixels leads to more precisely targeted and less perceptible attacks. On the COCO test dataset, our approach is 2.7 times better than the baseline in removing a detection, 3.86 times better in changing a detection, and 5.75 times better in triggering new, spurious, detections. The attacks generated by BlackCAtt are very close to the original image, and hence imperceptible, demonstrating the power of causal pixels.
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            <a href="https://www.alphaxiv.org/abs/2512.03715v1" target="_blank" rel="noopener noreferrer">
                DINO-RotateMatch：一种面向大规模三维重建中鲁棒图像匹配的旋转感知深度框架
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            DINO-RotateMatch: A Rotation-Aware Deep Framework for Robust Image Matching in Large-Scale 3D Reconstruction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kaichen Zhang, Tianxiang Sheng, Xuanming Shi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像匹配和3D重建技术，属于纯粹的视觉领域研究。虽然标题提到“大规模”，但这指的是3D重建的规模，而非推荐/搜索/广告系统所需的大规模数据处理。论文没有展示任何与推荐系统、搜索排序、广告投放或Transformer架构相关的潜在应用，完全属于不相关主题中的“Purely Vision”范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 12:05:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03715v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03715v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper presents DINO-RotateMatch, a deep-learning framework designed to address the chal lenges of image matching in large-scale 3D reconstruction from unstructured Internet images. The method integrates a dataset-adaptive image pairing strategy with rotation-aware keypoint extraction and matching. DINO is employed to retrieve semantically relevant image pairs in large collections, while rotation-based augmentation captures orientation-dependent local features using ALIKED and Light Glue. Experiments on the Kaggle Image Matching Challenge 2025 demonstrate consistent improve ments in mean Average Accuracy (mAA), achieving a Silver Award (47th of 943 teams). The results confirm that combining self-supervised global descriptors with rotation-enhanced local matching offers a robust and scalable solution for large-scale 3D reconstruction.
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            <a href="https://www.alphaxiv.org/abs/2512.03687v1" target="_blank" rel="noopener noreferrer">
                主动视觉感知：机遇与挑战
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        <div class="mb-2 text-base text-gray-700">
            Active Visual Perception: Opportunities and Challenges
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yian Li, Xiaoyu Guo, Hao Zhang, Shuiwang Li, Xiaowei Dai
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于视觉感知领域，属于纯粹的计算机视觉研究方向，没有提及任何与推荐系统、搜索或广告相关的应用场景。标题中的“主动视觉感知”主要涉及机器人视觉、场景理解等方向，与您关注的LLM技术、Transformer架构、推荐系统核心进展等焦点领域没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:27:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03687v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03687v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Active visual perception refers to the ability of a system to dynamically engage with its environment through sensing and action, allowing it to modify its behavior in response to specific goals or uncertainties. Unlike passive systems that rely solely on visual data, active visual perception systems can direct attention, move sensors, or interact with objects to acquire more informative data. This approach is particularly powerful in complex environments where static sensing methods may not provide sufficient information. Active visual perception plays a critical role in numerous applications, including robotics, autonomous vehicles, human-computer interaction, and surveillance systems. However, despite its significant promise, there are several challenges that need to be addressed, including real-time processing of complex visual data, decision-making in dynamic environments, and integrating multimodal sensory inputs. This paper explores both the opportunities and challenges inherent in active visual perception, providing a comprehensive overview of its potential, current research, and the obstacles that must be overcome for broader adoption.
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            <a href="https://www.alphaxiv.org/abs/2512.03683v1" target="_blank" rel="noopener noreferrer">
                GaussianBlender：具有解耦潜在空间的三维高斯模型的即时风格化
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            GaussianBlender: Instant Stylization of 3D Gaussians with Disentangled Latent Spaces
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Melis Ocal, Xiaoyan Xing, Yue Li, Ngo Anh Vien, Sezer Karaoglu, Theo Gevers
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及3D视觉和风格化技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术无关。标题中提到的三维高斯模型和风格化处理没有显示出在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 11:23:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03683v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03683v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    3D stylization is central to game development, virtual reality, and digital arts, where the demand for diverse assets calls for scalable methods that support fast, high-fidelity manipulation. Existing text-to-3D stylization methods typically distill from 2D image editors, requiring time-intensive per-asset optimization and exhibiting multi-view inconsistency due to the limitations of current text-to-image models, which makes them impractical for large-scale production. In this paper, we introduce GaussianBlender, a pioneering feed-forward framework for text-driven 3D stylization that performs edits instantly at inference. Our method learns structured, disentangled latent spaces with controlled information sharing for geometry and appearance from spatially-grouped 3D Gaussians. A latent diffusion model then applies text-conditioned edits on these learned representations. Comprehensive evaluations show that GaussianBlender not only delivers instant, high-fidelity, geometry-preserving, multi-view consistent stylization, but also surpasses methods that require per-instance test-time optimization - unlocking practical, democratized 3D stylization at scale.
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                Colon-X：从多模态理解到临床推理推进智能结肠镜检查
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            Colon-X: Advancing Intelligent Colonoscopy from Multimodal Understanding to Clinical Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ge-Peng Ji, Jingyi Liu, Deng-Ping Fan, Nick Barnes
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域的结肠镜检查应用，属于医疗/生物医学领域，这在“无关主题”中被明确排除。虽然提到了“多模态理解”，但这特指医学影像与临床数据的结合，而非RecSys/Search/Ads中处理异构用户/上下文数据的通用技术。没有迹象表明其核心进展（如架构、效率）或应用与推荐、搜索或广告系统相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 10:55:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03667v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03667v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this study, we present Colon-X, an open initiative aimed at advancing multimodal intelligence in colonoscopy. We begin by constructing ColonVQA, the most comprehensive multimodal dataset ever built for colonoscopy, featuring over 1.1M+ visual question answering entries across 76 clinical findings and 18 multimodal tasks. Beyond serving as a community-wide data foundation, we further investigate a critical yet underexplored transition in colonoscopy - evolving from multimodal understanding to clinical reasoning: (a) To capture the current landscape of multimodal understanding behaviors, we systematically assess the generalizability of 22 multimodal large language models and examine their reliability under human-induced perturbations. The results reveal that clinical outputs from leading MLLMs remain far from robust and trustworthy. (b) To narrow this gap, we further explore reasoning-centric intelligence tailored for colonoscopy. Specifically, we curate ColonReason, a clinically grounded reasoning dataset annotated through a multi-expert debating pipeline, and develop ColonR1, the first R1-styled model incorporating task-adaptive rewarding and gradient-stable optimization techniques. Under data-scarce conditions, our ColonR1 achieves 56.61% overall accuracy, outperforming supervised fine-tuning by 25.22%, and sets a new reasoning-enabled baseline for multimodal colonoscopy analysis. All data and model resources are publicly available at https://github.com/ai4colonoscopy/Colon-X.
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            <a href="https://www.alphaxiv.org/abs/2512.03666v1" target="_blank" rel="noopener noreferrer">
                ToG-Bench：第一人称视角视频中的任务导向时空定位基准
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            ToG-Bench: Task-Oriented Spatio-Temporal Grounding in Egocentric Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qi'ao Xu, Tianwen Qian, Yuqian Fu, Kailing Li, Yang Jiao, Jiacheng Zhang, Xiaoli...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于第一人称视角视频中的时空定位基准，属于纯粹的计算机视觉领域研究。虽然涉及多模态数据（视频），但主要关注视觉时空定位任务，与推荐系统、搜索或广告的核心技术需求（如用户行为建模、内容理解、个性化排序等）没有直接关联。论文没有展示任何在推荐/搜索/广告领域的潜在应用可能性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 10:54:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03666v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03666v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    A core capability towards general embodied intelligence lies in localizing task-relevant objects from an egocentric perspective, formulated as Spatio-Temporal Video Grounding (STVG). Despite recent progress, existing STVG studies remain largely confined to object-centric and descriptive instructions, neglecting the task-oriented reasoning that is crucial for embodied agents to accomplish goal-directed interactions. To bridge this gap, we introduce \textbf{ToG-Bench}, the first task-oriented spatio-temporal video grounding benchmark for egocentric videos. ToG-Bench is characterized by three key features: (1) \textbf{Task-oriented Grounding}, which requires identifying and localizing objects based on intended tasks rather than straightforward descriptions; (2) \textbf{Explicit-Implicit Dual Grounding}, where target objects can be either explicitly mentioned or implicitly inferred by contextual reasoning; (3) \textbf{One-to-Many Grounding}, where a single instruction may correspond to multiple objects involved in task execution. Built upon videos sourced from ScanNet, ToG-Bench comprises 100 annotated clips with 2,704 task-oriented grounding instructions, constructed via a semi-automated pipeline that combines foundation model annotation and human refinement. In addition, we introduce a set of task-level evaluation metrics tailored for multi-object and explicit-implicit object grounding, and systematically benchmark seven state-of-the-art MLLMs. Extensive experiments reveal the intrinsic challenges of task-oriented STVG and substantial performance gaps across explicit-implicit and multi-object grounding, highlighting the difficulty of bridging perception and interaction in embodied scenarios. Data and code will be released at: \href{https://github.com/qaxuDev/ToG-Bench}{https://github.com/qaxuDev/ToG-Bench}..
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2512.03663v1" target="_blank" rel="noopener noreferrer">
                面向轻量化小图像分类的多尺度视觉提示方法
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Multi-Scale Visual Prompting for Lightweight Small-Image Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Salim Khazem
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视觉领域的小图像分类任务，属于纯粹的计算机视觉研究。虽然提到了'多尺度'和'轻量化'等通用技术概念，但缺乏与推荐系统、搜索或广告领域的直接关联，也没有涉及LLM技术、Transformer架构进展或异构数据处理等当前关注的核心方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 10:51:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03663v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03663v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Visual prompting has recently emerged as an efficient strategy to adapt vision models using lightweight, learnable parameters injected into the input space. However, prior work mainly targets large Vision Transformers and high-resolution datasets such as ImageNet. In contrast, small-image benchmarks like MNIST, Fashion-MNIST, and CIFAR-10 remain widely used in education, prototyping, and research, yet have received little attention in the context of prompting. In this paper, we introduce \textbf{Multi-Scale Visual Prompting (MSVP)}, a simple and generic module that learns a set of global, mid-scale, and local prompt maps fused with the input image via a lightweight $1 \times 1$ convolution. MSVP is backbone-agnostic, adds less than $0.02\%$ parameters, and significantly improves performance across CNN and Vision Transformer backbones. We provide a unified benchmark on MNIST, Fashion-MNIST, and CIFAR-10 using a simple CNN, ResNet-18, and a small Vision Transformer. Our method yields consistent improvements with negligible computational overhead. We further include ablations on prompt scales, fusion strategies, and backbone architectures, along with qualitative analyzes using prompt visualizations and Grad-CAM. Our results demonstrate that multi-scale prompting provides an effective inductive bias even on low-resolution images.
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            <a href="https://www.alphaxiv.org/abs/2512.03656v1" target="_blank" rel="noopener noreferrer">
                循环时间编码与混合深度集成方法用于多步能源预测
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            Cyclical Temporal Encoding and Hybrid Deep Ensembles for Multistep Energy Forecasting
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Salim Khazem, Houssam Kanso
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于能源预测领域，属于特定领域应用（能源/电力系统），与搜索、推荐或广告系统无直接关联。虽然涉及时间序列建模和深度集成方法，但这些技术本身并未明确指向或适用于推荐系统、搜索或广告领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 10:46:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03656v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03656v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate electricity consumption forecasting is essential for demand management and smart grid operations. This paper introduces a unified deep learning framework that integrates cyclical temporal encoding with hybrid LSTM-CNN architectures to enhance multistep energy forecasting. We systematically transform calendar-based attributes using sine cosine encodings to preserve periodic structure and evaluate their predictive relevance through correlation analysis. To exploit both long-term seasonal effects and short-term local patterns, we employ an ensemble model composed of an LSTM, a CNN, and a meta-learner of MLP regressors specialized for each forecast horizon. Using a one year national consumption dataset, we conduct an extensive experimental study including ablation analyses with and without cyclical encodings and calendar features and comparisons with established baselines from the literature. Results demonstrate consistent improvements across all seven forecast horizons, with our hybrid model achieving lower RMSE and MAE than individual architectures and prior methods. These findings confirm the benefit of combining cyclical temporal representations with complementary deep learning structures. To our knowledge, this is the first work to jointly evaluate temporal encodings, calendar-based features, and hybrid ensemble architectures within a unified short-term energy forecasting framework.
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            <a href="https://www.alphaxiv.org/abs/2512.03640v1" target="_blank" rel="noopener noreferrer">
                MKSNet：基于多核与双重注意力机制的遥感影像小目标检测先进方法
            </a>
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            MKSNet: Advanced Small Object Detection in Remote Sensing Imagery with Multi-Kernel and Dual Attention Mechanisms
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahao Zhang, Xiao Zhao, Guangyu Gao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感影像中的小目标检测，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术无直接关联。双重注意力机制虽为通用技术，但论文未展示其在异构数据处理或推荐/搜索场景中的应用潜力，因此不符合任何关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 10:22:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03640v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03640v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Deep convolutional neural networks (DCNNs) have substantially advanced object detection capabilities, particularly in remote sensing imagery. However, challenges persist, especially in detecting small objects where the high resolution of these images and the small size of target objects often result in a loss of critical information in the deeper layers of conventional CNNs. Additionally, the extensive spatial redundancy and intricate background details typical in remote-sensing images tend to obscure these small targets. To address these challenges, we introduce Multi-Kernel Selection Network (MKSNet), a novel network architecture featuring a novel Multi-Kernel Selection mechanism. The MKS mechanism utilizes large convolutional kernels to effectively capture an extensive range of contextual information. This innovative design allows for adaptive kernel size selection, significantly enhancing the network's ability to dynamically process and emphasize crucial spatial details for small object detection. Furthermore, MKSNet also incorporates a dual attention mechanism, merging spatial and channel attention modules. The spatial attention module adaptively fine-tunes the spatial weights of feature maps, focusing more intensively on relevant regions while mitigating background noise. Simultaneously, the channel attention module optimizes channel information selection, improving feature representation and detection accuracy. Empirical evaluations on the DOTA-v1.0 and HRSC2016 benchmark demonstrate that MKSNet substantially surpasses existing state-of-the-art models in detecting small objects in remote sensing images. These results highlight MKSNet's superior ability to manage the complexities associated with multi-scale and high-resolution image data, confirming its effectiveness and innovation in remote sensing object detection.
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            <a href="https://www.alphaxiv.org/abs/2512.03625v1" target="_blank" rel="noopener noreferrer">
                FeatureLens：一种基于图像特征检测对抗样本的高度可泛化与可解释框架
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            FeatureLens: A Highly Generalizable and Interpretable Framework for Detecting Adversarial Examples Based on Image Features
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhigang Yang, Yuan Liu, Jiawei Zhang, Puning Zhang, Xinqiang Ma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的对抗样本检测，属于安全/防御技术范畴，与推荐系统、搜索或广告的核心技术无关。虽然标题提到“高度可泛化”，但这是针对图像特征的泛化能力，而非推荐/搜索/广告领域所需的跨模态或序列建模能力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 10:02:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03625v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03625v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Although the remarkable performance of deep neural networks (DNNs) in image classification, their vulnerability to adversarial attacks remains a critical challenge. Most existing detection methods rely on complex and poorly interpretable architectures, which compromise interpretability and generalization. To address this, we propose FeatureLens, a lightweight framework that acts as a lens to scrutinize anomalies in image features. Comprising an Image Feature Extractor (IFE) and shallow classifiers (e.g., SVM, MLP, or XGBoost) with model sizes ranging from 1,000 to 30,000 parameters, FeatureLens achieves high detection accuracy ranging from 97.8% to 99.75% in closed-set evaluation and 86.17% to 99.6% in generalization evaluation across FGSM, PGD, CW, and DAmageNet attacks, using only 51 dimensional features. By combining strong detection performance with excellent generalization, interpretability, and computational efficiency, FeatureLens offers a practical pathway toward transparent and effective adversarial defense.
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            <a href="https://www.alphaxiv.org/abs/2512.03621v1" target="_blank" rel="noopener noreferrer">
                ReCamDriving：无需激光雷达的相机控制新型轨迹视频生成
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            ReCamDriving: LiDAR-Free Camera-Controlled Novel Trajectory Video Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaokun Li, Shuaixian Wang, Mantang Guo, Jiehui Huang, Taojun Ding, Mu Hu, Kaixua...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域的轨迹视频生成，属于纯粹的计算机视觉应用。虽然涉及多模态建模（相机控制），但其核心是3D视觉和自动驾驶，与推荐系统、搜索或广告领域没有直接关联，也不属于能够应用于这些领域的使能技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:55:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03621v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03621v1
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                    We propose ReCamDriving, a purely vision-based, camera-controlled novel-trajectory video generation framework. While repair-based methods fail to restore complex artifacts and LiDAR-based approaches rely on sparse and incomplete cues, ReCamDriving leverages dense and scene-complete 3DGS renderings for explicit geometric guidance, achieving precise camera-controllable generation. To mitigate overfitting to restoration behaviors when conditioned on 3DGS renderings, ReCamDriving adopts a two-stage training paradigm: the first stage uses camera poses for coarse control, while the second stage incorporates 3DGS renderings for fine-grained viewpoint and geometric guidance. Furthermore, we present a 3DGS-based cross-trajectory data curation strategy to eliminate the train-test gap in camera transformation patterns, enabling scalable multi-trajectory supervision from monocular videos. Based on this strategy, we construct the ParaDrive dataset, containing over 110K parallel-trajectory video pairs. Extensive experiments demonstrate that ReCamDriving achieves state-of-the-art camera controllability and structural consistency.
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            <a href="https://www.alphaxiv.org/abs/2512.03619v1" target="_blank" rel="noopener noreferrer">
                LAMP：用于可控视频生成的语言辅助运动规划
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            LAMP: Language-Assisted Motion Planning for Controllable Video Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Muhammed Burak Kizil, Enes Sanli, Niloy J. Mitra, Erkut Erdem, Aykut Erdem, Duyg...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于可控视频生成，属于计算机视觉和内容生成领域。虽然涉及语言辅助，但主要应用于视频生成而非推荐系统、搜索或广告。根据您的关注点，这属于不相关的AIGC/内容生成主题，没有明确的推荐/搜索/广告应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:51:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03619v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03619v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Video generation has achieved remarkable progress in visual fidelity and controllability, enabling conditioning on text, layout, or motion. Among these, motion control - specifying object dynamics and camera trajectories - is essential for composing complex, cinematic scenes, yet existing interfaces remain limited. We introduce LAMP that leverages large language models (LLMs) as motion planners to translate natural language descriptions into explicit 3D trajectories for dynamic objects and (relatively defined) cameras. LAMP defines a motion domain-specific language (DSL), inspired by cinematography conventions. By harnessing program synthesis capabilities of LLMs, LAMP generates structured motion programs from natural language, which are deterministically mapped to 3D trajectories. We construct a large-scale procedural dataset pairing natural text descriptions with corresponding motion programs and 3D trajectories. Experiments demonstrate LAMP's improved performance in motion controllability and alignment with user intent compared to state-of-the-art alternatives establishing the first framework for generating both object and camera motions directly from natural language specifications.
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            <a href="https://www.alphaxiv.org/abs/2512.03601v1" target="_blank" rel="noopener noreferrer">
                Motion4D：学习用于4D场景理解的3D一致性运动与语义
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            Motion4D: Learning 3D-Consistent Motion and Semantics for 4D Scene Understanding
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haoran Zhou, Gim Hee Lee
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于4D场景理解中的3D运动建模和语义分析，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然标题提及“学习”，但内容涉及视觉场景理解，不符合当前关注的LLM技术、Transformer架构进展或异构数据建模等方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:32:56
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                <a href="https://arxiv.org/abs/2512.03601v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03601v1
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                    Recent advancements in foundation models for 2D vision have substantially improved the analysis of dynamic scenes from monocular videos. However, despite their strong generalization capabilities, these models often lack 3D consistency, a fundamental requirement for understanding scene geometry and motion, thereby causing severe spatial misalignment and temporal flickering in complex 3D environments. In this paper, we present Motion4D, a novel framework that addresses these challenges by integrating 2D priors from foundation models into a unified 4D Gaussian Splatting representation. Our method features a two-part iterative optimization framework: 1) Sequential optimization, which updates motion and semantic fields in consecutive stages to maintain local consistency, and 2) Global optimization, which jointly refines all attributes for long-term coherence. To enhance motion accuracy, we introduce a 3D confidence map that dynamically adjusts the motion priors, and an adaptive resampling process that inserts new Gaussians into under-represented regions based on per-pixel RGB and semantic errors. Furthermore, we enhance semantic coherence through an iterative refinement process that resolves semantic inconsistencies by alternately optimizing the semantic fields and updating prompts of SAM2. Extensive evaluations demonstrate that our Motion4D significantly outperforms both 2D foundation models and existing 3D-based approaches across diverse scene understanding tasks, including point-based tracking, video object segmentation, and novel view synthesis. Our code is available at https://hrzhou2.github.io/motion4d-web/.
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            <a href="https://www.alphaxiv.org/abs/2512.03598v1" target="_blank" rel="noopener noreferrer">
                用于牙齿重建的记忆引导点云补全
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            Memory-Guided Point Cloud Completion for Dental Reconstruction
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jianan Sun, Yukang Huang, Dongzhihan Wang, Mingyu Fan
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及牙齿重建的3D点云补全技术，属于医学/牙科领域的特定应用，与搜索、推荐、广告等核心领域无关。虽然点云处理可能涉及3D视觉技术，但论文明确聚焦于牙齿重建这一医疗应用场景，没有显示出与推荐系统、搜索或广告相关的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:31:07
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                <a href="https://arxiv.org/abs/2512.03598v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03598v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Partial dental point clouds often suffer from large missing regions caused by occlusion and limited scanning views, which bias encoder-only global features and force decoders to hallucinate structures. We propose a retrieval-augmented framework for tooth completion that integrates a prototype memory into standard encoder--decoder pipelines. After encoding a partial input into a global descriptor, the model retrieves the nearest manifold prototype from a learnable memory and fuses it with the query feature through confidence-gated weighting before decoding. The memory is optimized end-to-end and self-organizes into reusable tooth-shape prototypes without requiring tooth-position labels, thereby providing structural priors that stabilize missing-region inference and free decoder capacity for detail recovery. The module is plug-and-play and compatible with common completion backbones, while keeping the same training losses. Experiments on a self-processed Teeth3DS benchmark demonstrate consistent improvements in Chamfer Distance, with visualizations showing sharper cusps, ridges, and interproximal transitions. Our approach provides a simple yet effective way to exploit cross-sample regularities for more accurate and faithful dental point-cloud completion.
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            <a href="https://www.alphaxiv.org/abs/2512.03597v1" target="_blank" rel="noopener noreferrer">
                HBFormer：一种用于微肿瘤和微型器官分割的混合桥接Transformer
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            HBFormer: A Hybrid-Bridge Transformer for Microtumor and Miniature Organ Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fuchen Zheng, Xinyi Chen, Weixuan Li, Quanjun Li, Junhua Zhou, Xiaojiao Guo, Xuh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学图像分割（微肿瘤和微型器官），属于医学/生物领域的特定应用，与用户关注的推荐系统、搜索、广告等核心领域完全无关。虽然使用了Transformer架构，但其应用场景被明确限定在医疗领域，没有显示出任何向推荐/搜索/广告领域迁移的潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:30:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03597v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03597v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Medical image segmentation is a cornerstone of modern clinical diagnostics. While Vision Transformers that leverage shifted window-based self-attention have established new benchmarks in this field, they are often hampered by a critical limitation: their localized attention mechanism struggles to effectively fuse local details with global context. This deficiency is particularly detrimental to challenging tasks such as the segmentation of microtumors and miniature organs, where both fine-grained boundary definition and broad contextual understanding are paramount. To address this gap, we propose HBFormer, a novel Hybrid-Bridge Transformer architecture. The 'Hybrid' design of HBFormer synergizes a classic U-shaped encoder-decoder framework with a powerful Swin Transformer backbone for robust hierarchical feature extraction. The core innovation lies in its 'Bridge' mechanism, a sophisticated nexus for multi-scale feature integration. This bridge is architecturally embodied by our novel Multi-Scale Feature Fusion (MFF) decoder. Departing from conventional symmetric designs, the MFF decoder is engineered to fuse multi-scale features from the encoder with global contextual information. It achieves this through a synergistic combination of channel and spatial attention modules, which are constructed from a series of dilated and depth-wise convolutions. These components work in concert to create a powerful feature bridge that explicitly captures long-range dependencies and refines object boundaries with exceptional precision. Comprehensive experiments on challenging medical image segmentation datasets, including multi-organ, liver tumor, and bladder tumor benchmarks, demonstrate that HBFormer achieves state-of-the-art results, showcasing its outstanding capabilities in microtumor and miniature organ segmentation. Code and models are available at: https://github.com/lzeeorno/HBFormer.
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            <a href="https://www.alphaxiv.org/abs/2512.03593v1" target="_blank" rel="noopener noreferrer">
                CloseUpAvatar：采用多尺度纹理混合的高保真可动画全身虚拟化身
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            CloseUpAvatar: High-Fidelity Animatable Full-Body Avatars with Mixture of Multi-Scale Textures
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>David Svitov, Pietro Morerio, Lourdes Agapito, Alessio Del Bue
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机图形学中的高保真3D虚拟化身创建和动画技术，属于纯粹的视觉/图形领域。虽然标题提到“多尺度纹理混合”，但这与推荐系统、搜索或广告中的Transformer架构、LLM应用或异构数据统一建模没有直接关联。该技术主要应用于游戏、虚拟现实或数字内容创作，而非RecSys/Search/Ads领域的核心问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:25:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03593v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03593v1
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present a CloseUpAvatar - a novel approach for articulated human avatar representation dealing with more general camera motions, while preserving rendering quality for close-up views. CloseUpAvatar represents an avatar as a set of textured planes with two sets of learnable textures for low and high-frequency detail. The method automatically switches to high-frequency textures only for cameras positioned close to the avatar's surface and gradually reduces their impact as the camera moves farther away. Such parametrization of the avatar enables CloseUpAvatar to adjust rendering quality based on camera distance ensuring realistic rendering across a wider range of camera orientations than previous approaches. We provide experiments using the ActorsHQ dataset with high-resolution input images. CloseUpAvatar demonstrates both qualitative and quantitative improvements over existing methods in rendering from novel wide range camera positions, while maintaining high FPS by limiting the number of required primitives.
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            <a href="https://www.alphaxiv.org/abs/2512.03592v1" target="_blank" rel="noopener noreferrer">
                利用几何深度学习中的超图进行3D RNA逆折叠
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            Harnessing Hypergraphs in Geometric Deep Learning for 3D RNA Inverse Folding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guang Yang, Lei Fan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及生物学领域（3D RNA逆折叠），这属于明确的无关主题。虽然提到了几何深度学习和超图，但这些技术被应用于与推荐系统、搜索或广告无关的特定生物领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:23:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03592v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03592v1
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The RNA inverse folding problem, a key challenge in RNA design, involves identifying nucleotide sequences that can fold into desired secondary structures, which are critical for ensuring molecular stability and function. The inherent complexity of this task stems from the intricate relationship between sequence and structure, making it particularly challenging. In this paper, we propose a framework, named HyperRNA, a generative model with an encoder-decoder architecture that leverages hypergraphs to design RNA sequences. Specifically, our HyperRNA model consists of three main components: preprocessing, encoding and decoding. In the preprocessing stage, graph structures are constructed by extracting the atom coordinates of RNA backbone based on 3-bead coarse-grained representation. The encoding stage processes these graphs, capturing higher order dependencies and complex biomolecular interactions using an attention embedding module and a hypergraph-based encoder. Finally, the decoding stage generates the RNA sequence in an autoregressive manner. We conducted quantitative and qualitative experiments on the PDBBind and RNAsolo datasets to evaluate the inverse folding task for RNA sequence generation and RNA-protein complex sequence generation. The experimental results demonstrate that HyperRNA not only outperforms existing RNA design methods but also highlights the potential of leveraging hypergraphs in RNA engineering.
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            <a href="https://www.alphaxiv.org/abs/2512.03580v1" target="_blank" rel="noopener noreferrer">
                动态光学测试用于机器人识别（DOT-BI）：一种识别调查和在线流程中机器人的简单检查方法
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            Dynamic Optical Test for Bot Identification (DOT-BI): A simple check to identify bots in surveys and online processes
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Malte Bleeker, Mauro Gotsch
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及机器人识别和在线流程安全，属于安全/隐私领域，与用户关注的核心推荐系统、搜索广告、LLM技术、Transformer架构或异构数据建模等主题无关。标题中提到的'调查和在线流程'可能涉及用户体验，但核心焦点是机器人检测，这属于被明确排除的'安全、隐私'等非技术性话题范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:03:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03580v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03580v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CR</span></div>
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                    We propose the Dynamic Optical Test for Bot Identification (DOT-BI): a quick and easy method that uses human perception of motion to differentiate between human respondents and automated systems in surveys and online processes. In DOT-BI, a 'hidden' number is displayed with the same random black-and-white pixel texture as its background. Only the difference in motion and scale between the number and the background makes the number perceptible to humans across frames, while frame-by-frame algorithmic processing yields no meaningful signal. We conducted two preliminary assessments. Firstly, state-of-the-art, video-capable, multimodal models (GPT-5-Thinking and Gemini 2.5 Pro) fail to extract the correct value, even when given explicit instructions about the mechanism. Secondly, in an online survey (n=182), 99.5% (181/182) of participants solved the task, with an average end-to-end completion time of 10.7 seconds; a supervised lab study (n=39) found no negative effects on perceived ease-of-use or completion time relative to a control. We release code to generate tests and 100+ pre-rendered variants to facilitate adoption in surveys and online processes.
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            <a href="https://www.alphaxiv.org/abs/2512.03577v1" target="_blank" rel="noopener noreferrer">
                用于配对免疫组织化学与组织病理学切片表示学习的跨染色对比学习
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            Cross-Stain Contrastive Learning for Paired Immunohistochemistry and Histopathology Slide Representation Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yizhi Zhang, Lei Fan, Zhulin Tao, Donglin Di, Yang Song, Sidong Liu, Cong Cong
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学图像分析领域（免疫组织化学和组织病理学），属于明确的生物学/医学应用范畴，这在无关主题中被明确排除。虽然对比学习是一种通用技术，但论文专注于特定医学领域的数据类型和处理方法，没有显示出对推荐系统、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 09:00:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03577v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03577v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Universal, transferable whole-slide image (WSI) representations are central to computational pathology. Incorporating multiple markers (e.g., immunohistochemistry, IHC) alongside H&E enriches H&E-based features with diverse, biologically meaningful information. However, progress is limited by the scarcity of well-aligned multi-stain datasets. Inter-stain misalignment shifts corresponding tissue across slides, hindering consistent patch-level features and degrading slide-level embeddings. To address this, we curated a slide-level aligned, five-stain dataset (H&E, HER2, KI67, ER, PGR) to enable paired H&E-IHC learning and robust cross-stain representation. Leveraging this dataset, we propose Cross-Stain Contrastive Learning (CSCL), a two-stage pretraining framework with a lightweight adapter trained using patch-wise contrastive alignment to improve the compatibility of H&E features with corresponding IHC-derived contextual cues, and slide-level representation learning with Multiple Instance Learning (MIL), which uses a cross-stain attention fusion module to integrate stain-specific patch features and a cross-stain global alignment module to enforce consistency among slide-level embeddings across different stains. Experiments on cancer subtype classification, IHC biomarker status classification, and survival prediction show consistent gains, yielding high-quality, transferable H&E slide-level representations. The code and data are available at https://github.com/lily-zyz/CSCL.
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            <a href="https://www.alphaxiv.org/abs/2512.03575v1" target="_blank" rel="noopener noreferrer">
                UniComp：基于信息唯一性重新思考视频压缩
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            UniComp: Rethinking Video Compression Through Informational Uniqueness
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chao Yuan, Shimin Chen, Minliang Lin, Limeng Qiao, Guanglu Wan, Lin Ma
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频压缩技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。即使考虑其作为潜在的多模态数据处理技术，视频压缩本身并不涉及用户行为建模、内容排序或广告投放等关键任务，因此与当前关注点高度不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 08:56:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03575v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03575v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Distinct from attention-based compression methods, this paper presents an information uniqueness driven video compression framework, termed UniComp, which aims to maximize the information fidelity of video representations under constrained computational budgets. Starting from the information-theoretic perspective, we formulate the vision compression as an optimization problem that minimizes conditional entropy (reconstruction error) between retained and full tokens. To achieve this, we introduce the notion of information uniqueness to measure intrinsic redundancy among tokens to link with reconstruction error. Based on uniqueness, we design three modules-Frame Group Fusion, Token Allocation, and Spatial Dynamic Compression-that progressively perform semantic frame grouping, adaptive resource allocation, and fine-grained spatial compression. Extensive experiments demonstrate that UniComp consistently outperforms existing compression methods in preserving essential visual tokens under limited computational budgets, highlighting the pivotal role of information uniqueness in token compression efficacy.
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            <a href="https://www.alphaxiv.org/abs/2512.03574v1" target="_blank" rel="noopener noreferrer">
                全局-局部感知的场景文本编辑
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            Global-Local Aware Scene Text Editing
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fuxiang Yang, Tonghua Su, Donglin Di, Yin Chen, Xiangqian Wu, Zhongjie Wang, Lei...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向计算机视觉中的场景文本编辑任务，属于纯粹的视觉处理范畴。虽然标题中的'全局-局部'概念可能涉及多尺度建模，但该技术专注于图像中的文本修改，与推荐系统、搜索或广告的核心排名、检索、建模需求没有直接关联，也不涉及LLM或Transformer架构在文本序列处理方面的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 08:56:01
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                <a href="https://arxiv.org/abs/2512.03574v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03574v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Scene Text Editing (STE) involves replacing text in a scene image with new target text while preserving both the original text style and background texture. Existing methods suffer from two major challenges: inconsistency and length-insensitivity. They often fail to maintain coherence between the edited local patch and the surrounding area, and they struggle to handle significant differences in text length before and after editing. To tackle these challenges, we propose an end-to-end framework called Global-Local Aware Scene Text Editing (GLASTE), which simultaneously incorporates high-level global contextual information along with delicate local features. Specifically, we design a global-local combination structure, joint global and local losses, and enhance text image features to ensure consistency in text style within local patches while maintaining harmony between local and global areas. Additionally, we express the text style as a vector independent of the image size, which can be transferred to target text images of various sizes. We use an affine fusion to fill target text images into the editing patch while maintaining their aspect ratio unchanged. Extensive experiments on real-world datasets validate that our GLASTE model outperforms previous methods in both quantitative metrics and qualitative results and effectively mitigates the two challenges.
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                GAOT：通过文本引导扩散模型生成铰接式物体
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            GAOT: Generating Articulated Objects Through Text-Guided Diffusion Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Sun, Lei Fan, Donglin Di, Shaohui Liu
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明研究内容为使用文本引导扩散模型生成铰接式物体，这属于计算机视觉和3D内容生成领域。虽然涉及生成模型，但主要关注3D物体生成而非推荐系统、搜索或广告应用，且没有明确提及异构数据处理或Transformer架构改进。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 08:44:17
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                <a href="https://arxiv.org/abs/2512.03566v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03566v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.MM</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Articulated object generation has seen increasing advancements, yet existing models often lack the ability to be conditioned on text prompts. To address the significant gap between textual descriptions and 3D articulated object representations, we propose GAOT, a three-phase framework that generates articulated objects from text prompts, leveraging diffusion models and hypergraph learning in a three-step process. First, we fine-tune a point cloud generation model to produce a coarse representation of objects from text prompts. Given the inherent connection between articulated objects and graph structures, we design a hypergraph-based learning method to refine these coarse representations, representing object parts as graph vertices. Finally, leveraging a diffusion model, the joints of articulated objects-represented as graph edges-are generated based on the object parts. Extensive qualitative and quantitative experiments on the PartNet-Mobility dataset demonstrate the effectiveness of our approach, achieving superior performance over previous methods.
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            <a href="https://www.alphaxiv.org/abs/2512.03556v1" target="_blank" rel="noopener noreferrer">
                RoboScape-R：基于强化学习的通用机器人训练的统一奖励-观测世界模型
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            RoboScape-R: Unified Reward-Observation World Models for Generalizable Robotics Training via RL
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yinzhou Tang, Yu Shang, Yinuo Chen, Bingwen Wei, Xin Zhang, Shu'ang Yu, Liangzhi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于机器人强化学习训练，属于纯粹的机器人学领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及世界模型和强化学习，但缺乏与推荐/搜索/广告应用的明确联系，属于您指定的不相关主题范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 08:24:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03556v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03556v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    Achieving generalizable embodied policies remains a key challenge. Traditional policy learning paradigms, including both Imitation Learning (IL) and Reinforcement Learning (RL), struggle to cultivate generalizability across diverse scenarios. While IL policies often overfit to specific expert trajectories, RL suffers from the inherent lack of a unified and general reward signal necessary for effective multi-scene generalization. We posit that the world model is uniquely capable of serving as a universal environment proxy to address this limitation. However, current world models primarily focus on their ability to predict observations and still rely on task-specific, handcrafted reward functions, thereby failing to provide a truly general training environment. Toward this problem, we propose RoboScape-R, a framework leveraging the world model to serve as a versatile, general-purpose proxy for the embodied environment within the RL paradigm. We introduce a novel world model-based general reward mechanism that generates ''endogenous'' rewards derived from the model's intrinsic understanding of real-world state transition dynamics. Extensive experiments demonstrate that RoboScape-R effectively addresses the limitations of traditional RL methods by providing an efficient and general training environment that substantially enhances the generalization capability of embodied policies. Our approach offers critical insights into utilizing the world model as an online training strategy and achieves an average 37.5% performance improvement over baselines under out-of-domain scenarios.
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            <a href="https://www.alphaxiv.org/abs/2512.03540v1" target="_blank" rel="noopener noreferrer">
                CookAnything：一种用于灵活且一致的多步骤食谱图像生成的框架
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            CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruoxuan Zhang, Bin Wen, Hongxia Xie, Yi Yao, Songhan Zuo, Jian-Yu Jiang-Lin, Hon...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于食谱图像生成，属于纯粹的AIGC/内容生成领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及多步骤生成，但缺乏对RecSys/Search/Ads应用的明确联系，因此不符合任何关注点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 08:01:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03540v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03540v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Cooking is a sequential and visually grounded activity, where each step such as chopping, mixing, or frying carries both procedural logic and visual semantics. While recent diffusion models have shown strong capabilities in text-to-image generation, they struggle to handle structured multi-step scenarios like recipe illustration. Additionally, current recipe illustration methods are unable to adjust to the natural variability in recipe length, generating a fixed number of images regardless of the actual instructions structure. To address these limitations, we present CookAnything, a flexible and consistent diffusion-based framework that generates coherent, semantically distinct image sequences from textual cooking instructions of arbitrary length. The framework introduces three key components: (1) Step-wise Regional Control (SRC), which aligns textual steps with corresponding image regions within a single denoising process; (2) Flexible RoPE, a step-aware positional encoding mechanism that enhances both temporal coherence and spatial diversity; and (3) Cross-Step Consistency Control (CSCC), which maintains fine-grained ingredient consistency across steps. Experimental results on recipe illustration benchmarks show that CookAnything performs better than existing methods in training-based and training-free settings. The proposed framework supports scalable, high-quality visual synthesis of complex multi-step instructions and holds significant potential for broad applications in instructional media, and procedural content creation.
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            <a href="https://www.alphaxiv.org/abs/2512.03532v1" target="_blank" rel="noopener noreferrer">
                OpenTrack3D：迈向精确且可泛化的开放词汇3D实例分割
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            OpenTrack3D: Towards Accurate and Generalizable Open-Vocabulary 3D Instance Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhishan Zhou, Siyuan Wei, Zengran Wang, Chunjie Wang, Xiaosheng Yan, Xiao Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉中的开放词汇实例分割，属于纯粹的计算机视觉研究。虽然标题包含'开放词汇'概念，但核心是3D视觉任务，与推荐系统、搜索或广告的异构数据处理、序列建模或排名优化没有直接关联。该技术主要应用于机器人、自动驾驶等3D感知领域，而非推荐/搜索/广告的核心技术栈。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 07:51:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03532v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03532v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generalizing open-vocabulary 3D instance segmentation (OV-3DIS) to diverse, unstructured, and mesh-free environments is crucial for robotics and AR/VR, yet remains a significant challenge. We attribute this to two key limitations of existing methods: (1) proposal generation relies on dataset-specific proposal networks or mesh-based superpoints, rendering them inapplicable in mesh-free scenarios and limiting generalization to novel scenes; and (2) the weak textual reasoning of CLIP-based classifiers, which struggle to recognize compositional and functional user queries. To address these issues, we introduce OpenTrack3D, a generalizable and accurate framework. Unlike methods that rely on pre-generated proposals, OpenTrack3D employs a novel visual-spatial tracker to construct cross-view consistent object proposals online. Given an RGB-D stream, our pipeline first leverages a 2D open-vocabulary segmenter to generate masks, which are lifted to 3D point clouds using depth. Mask-guided instance features are then extracted using DINO feature maps, and our tracker fuses visual and spatial cues to maintain instance consistency. The core pipeline is entirely mesh-free, yet we also provide an optional superpoints refinement module to further enhance performance when scene mesh is available. Finally, we replace CLIP with a multi-modal large language model (MLLM), significantly enhancing compositional reasoning for complex user queries. Extensive experiments on diverse benchmarks, including ScanNet200, Replica, ScanNet++, and SceneFun3D, demonstrate state-of-the-art performance and strong generalization capabilities.
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            <a href="https://www.alphaxiv.org/abs/2512.03522v1" target="_blank" rel="noopener noreferrer">
                MSG-Loc：基于多标签似然语义图匹配的对象级全局定位
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            MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gihyeon Lee, Jungwoo Lee, Juwon Kim, Young-Sik Shin, Younggun Cho
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的对象级全局定位，属于纯粹的视觉定位技术，与推荐系统、搜索或广告的核心领域进展、LLM技术应用、Transformer架构改进或异构数据统一建模均无直接关联。标题中提到的语义图匹配是计算机视觉领域的特定方法，没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 07:28:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03522v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03522v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    Robots are often required to localize in environments with unknown object classes and semantic ambiguity. However, when performing global localization using semantic objects, high semantic ambiguity intensifies object misclassification and increases the likelihood of incorrect associations, which in turn can cause significant errors in the estimated pose. Thus, in this letter, we propose a multi-label likelihood-based semantic graph matching framework for object-level global localization. The key idea is to exploit multi-label graph representations, rather than single-label alternatives, to capture and leverage the inherent semantic context of object observations. Based on these representations, our approach enhances semantic correspondence across graphs by combining the likelihood of each node with the maximum likelihood of its neighbors via context-aware likelihood propagation. For rigorous validation, data association and pose estimation performance are evaluated under both closed-set and open-set detection configurations. In addition, we demonstrate the scalability of our approach to large-vocabulary object categories in both real-world indoor scenes and synthetic environments.
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            <a href="https://www.alphaxiv.org/abs/2512.03520v1" target="_blank" rel="noopener noreferrer">
                FloodDiffusion：面向流式运动生成的定制化扩散驱动
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            FloodDiffusion: Tailored Diffusion Forcing for Streaming Motion Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiyi Cai, Yuhan Wu, Kunhang Li, You Zhou, Bo Zheng, Haiyang Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于运动生成（Motion Generation），属于计算机视觉和图形学领域，与推荐系统、搜索或广告的核心技术无关。扩散模型在该文中被应用于特定领域（运动生成），而非作为推荐/搜索/广告领域的使能技术。标题中未提及任何与异构数据建模、序列处理或推荐系统应用相关的元素。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 07:23:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03520v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03520v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present FloodDiffusion, a new framework for text-driven, streaming human motion generation. Given time-varying text prompts, FloodDiffusion generates text-aligned, seamless motion sequences with real-time latency. Unlike existing methods that rely on chunk-by-chunk or auto-regressive model with diffusion head, we adopt a diffusion forcing framework to model this time-series generation task under time-varying control events. We find that a straightforward implementation of vanilla diffusion forcing (as proposed for video models) fails to model real motion distributions. We demonstrate that to guarantee modeling the output distribution, the vanilla diffusion forcing must be tailored to: (i) train with a bi-directional attention instead of casual attention; (ii) implement a lower triangular time scheduler instead of a random one; (iii) utilize a continues time-varying way to introduce text conditioning. With these improvements, we demonstrate in the first time that the diffusion forcing-based framework achieves state-of-the-art performance on the streaming motion generation task, reaching an FID of 0.057 on the HumanML3D benchmark. Models, code, and weights are available. https://shandaai.github.io/FloodDiffusion/
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            <a href="https://www.alphaxiv.org/abs/2512.03510v1" target="_blank" rel="noopener noreferrer">
                CSMapping：面向自动驾驶的可扩展众包语义地图构建与拓扑推断
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            CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhijian Qiao, Zehuan Yu, Tong Li, Chih-Chung Chou, Wenchao Ding, Shaojie Shen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域的语义地图构建和拓扑推断，属于计算机视觉和机器人学的具体应用。虽然涉及大规模数据处理，但其核心是自动驾驶的感知和地图构建，与推荐系统、搜索或广告的排名、建模、架构创新等焦点领域没有直接关联。</p>
        </div>
        
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 07:06:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03510v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03510v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Crowdsourcing enables scalable autonomous driving map construction, but low-cost sensor noise hinders quality from improving with data volume. We propose CSMapping, a system that produces accurate semantic maps and topological road centerlines whose quality consistently increases with more crowdsourced data. For semantic mapping, we train a latent diffusion model on HD maps (optionally conditioned on SD maps) to learn a generative prior of real-world map structure, without requiring paired crowdsourced/HD-map supervision. This prior is incorporated via constrained MAP optimization in latent space, ensuring robustness to severe noise and plausible completion in unobserved areas. Initialization uses a robust vectorized mapping module followed by diffusion inversion; optimization employs efficient Gaussian-basis reparameterization, projected gradient descent zobracket multi-start, and latent-space factor-graph for global consistency. For topological mapping, we apply confidence-weighted k-medoids clustering and kinematic refinement to trajectories, yielding smooth, human-like centerlines robust to trajectory variation. Experiments on nuScenes, Argoverse 2, and a large proprietary dataset achieve state-of-the-art semantic and topological mapping performance, with thorough ablation and scalability studies.
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            <a href="https://www.alphaxiv.org/abs/2512.03509v1" target="_blank" rel="noopener noreferrer">
                基于计算机视觉的非洲节拍舞蹈动作分析：结合YOLO与Segment Anything模型的验证性框架
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            AfroBeats Dance Movement Analysis Using Computer Vision: A Proof-of-Concept Framework Combining YOLO and Segment Anything Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kwaku Opoku-Ware, Gideon Opoku
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于计算机视觉在舞蹈动作分析领域的应用，属于纯粹的视觉研究范畴。虽然提到了YOLO和Segment Anything模型，但应用场景（舞蹈分析）与推荐系统、搜索或广告领域没有直接关联，也不涉及LLM技术或Transformer架构的进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 07:06:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03509v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03509v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper presents a preliminary investigation into automated dance movement analysis using contemporary computer vision techniques. We propose a proof-of-concept framework that integrates YOLOv8 and v11 for dancer detection with the Segment Anything Model (SAM) for precise segmentation, enabling the tracking and quantification of dancer movements in video recordings without specialized equipment or markers. Our approach identifies dancers within video frames, counts discrete dance steps, calculates spatial coverage patterns, and measures rhythm consistency across performance sequences. Testing this framework on a single 49-second recording of Ghanaian AfroBeats dance demonstrates technical feasibility, with the system achieving approximately 94% detection precision and 89% recall on manually inspected samples. The pixel-level segmentation provided by SAM, achieving approximately 83% intersection-over-union with visual inspection, enables motion quantification that captures body configuration changes beyond what bounding-box approaches can represent. Analysis of this preliminary case study indicates that the dancer classified as primary by our system executed 23% more steps with 37% higher motion intensity and utilized 42% more performance space compared to dancers classified as secondary. However, this work represents an early-stage investigation with substantial limitations including single-video validation, absence of systematic ground truth annotations, and lack of comparison with existing pose estimation methods. We present this framework to demonstrate technical feasibility, identify promising directions for quantitative dance metrics, and establish a foundation for future systematic validation studies.
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            <a href="https://www.alphaxiv.org/abs/2512.03508v1" target="_blank" rel="noopener noreferrer">
                利用语言驱动领域泛化中的领域特性进行语义分割
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            Exploiting Domain Properties in Language-Driven Domain Generalization for Semantic Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Seogkyu Jeon, Kibeom Hong, Hyeran Byun
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于语义分割的计算机视觉任务，属于纯粹的视觉领域研究。虽然涉及语言驱动方法，但核心是视觉领域的领域泛化问题，与推荐系统、搜索或广告的异构数据处理没有直接关联。标题中没有任何元素表明该技术可应用于推荐、搜索或广告场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 06:58:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03508v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03508v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent domain generalized semantic segmentation (DGSS) studies have achieved notable improvements by distilling semantic knowledge from Vision-Language Models (VLMs). However, they overlook the semantic misalignment between visual and textual contexts, which arises due to the rigidity of a fixed context prompt learned on a single source domain. To this end, we present a novel domain generalization framework for semantic segmentation, namely Domain-aware Prompt-driven Masked Transformer (DPMFormer). Firstly, we introduce domain-aware prompt learning to facilitate semantic alignment between visual and textual cues. To capture various domain-specific properties with a single source dataset, we propose domain-aware contrastive learning along with the texture perturbation that diversifies the observable domains. Lastly, to establish a framework resilient against diverse environmental changes, we have proposed the domain-robust consistency learning which guides the model to minimize discrepancies of prediction from original and the augmented images. Through experiments and analyses, we demonstrate the superiority of the proposed framework, which establishes a new state-of-the-art on various DGSS benchmarks. The code is available at https://github.com/jone1222/DPMFormer.
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            <a href="https://www.alphaxiv.org/abs/2512.03477v1" target="_blank" rel="noopener noreferrer">
                面向医学青光眼诊断的公平感知视觉语言模型微调
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            Fairness-Aware Fine-Tuning of Vision-Language Models for Medical Glaucoma Diagnosis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zijian Gu, Yuxi Liu, Zhenhao Zhang, Song Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确涉及医疗领域应用（青光眼诊断），属于明确的无关主题。虽然标题包含'视觉语言模型'，但论文专注于医疗诊断而非推荐/搜索/广告应用，且涉及公平性主题，这也是明确的无关领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 06:09:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03477v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03477v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Vision-language models achieve expert-level performance on medical imaging tasks but exhibit significant diagnostic accuracy disparities across demographic groups. We introduce fairness-aware Low-Rank Adaptation for medical VLMs, combining parameter efficiency with explicit fairness optimization. Our key algorithmic contribution is a differentiable MaxAccGap loss that enables end-to-end optimization of accuracy parity across demographic groups. We propose three methods: FR-LoRA integrates MaxAccGap regularization into the training objective, GR-LoRA applies inverse frequency weighting to balance gradient contributions, and Hybrid-LoRA combines both mechanisms.Evaluated on 10,000 glaucoma fundus images, GR-LoRA reduces diagnostic accuracy disparities by 69% while maintaining 53.15% overall accuracy. Ablation studies reveal that strong regularization strength achieves optimal fairness with minimal accuracy trade-off, and race-specific optimization yields 60% disparity reduction. Our approach requires only 0.24% trainable parameters, enabling practical deployment of fair medical AI in resource-constrained healthcare settings.
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            <a href="https://www.alphaxiv.org/abs/2512.03470v1" target="_blank" rel="noopener noreferrer">
                用于红外小目标检测的差分分解网络
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            Difference Decomposition Networks for Infrared Small Target Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chen Hu, Mingyu Zhou, Shuai Yuan, Hongbo Hu, Xiangyu Qiu, Junhai Luo, Tian Pu, X...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向红外小目标检测，这属于计算机视觉中的特定领域应用，与推荐系统、搜索或广告没有直接关联。标题中提到的差分分解网络可能涉及视觉处理技术，但未表明与异构数据统一建模、Transformer架构改进或LLM应用有任何联系，因此与当前关注点高度不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:52:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03470v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03470v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Infrared small target detection (ISTD) faces two major challenges: a lack of discernible target texture and severe background clutter, which results in the background obscuring the target. To enhance targets and suppress backgrounds, we propose the Basis Decomposition Module (BDM) as an extensible and lightweight module based on basis decomposition, which decomposes a complex feature into several basis features and enhances certain information while eliminating redundancy. Extending BDM leads to a series of modules, including the Spatial Difference Decomposition Module (SD$^\mathrm{2}$M), Spatial Difference Decomposition Downsampling Module (SD$^\mathrm{3}$M), and Temporal Difference Decomposition Module (TD$^\mathrm{2}$M). Based on these modules, we develop the Spatial Difference Decomposition Network (SD$^\mathrm{2}$Net) for single-frame ISTD (SISTD) and the Spatiotemporal Difference Decomposition Network (STD$^\mathrm{2}$Net) for multi-frame ISTD (MISTD). SD$^\mathrm{2}$Net integrates SD$^\mathrm{2}$M and SD$^\mathrm{3}$M within an adapted U-shaped architecture. We employ TD$^\mathrm{2}$M to introduce motion information, which transforms SD$^\mathrm{2}$Net into STD$^\mathrm{2}$Net. Extensive experiments on SISTD and MISTD datasets demonstrate state-of-the-art (SOTA) performance. On the SISTD task, SD$^\mathrm{2}$Net performs well compared to most established networks. On the MISTD datasets, STD$^\mathrm{2}$Net achieves a mIoU of 87.68\%, outperforming SD$^\mathrm{2}$Net, which achieves a mIoU of 64.97\%. Our codes are available: https://github.com/greekinRoma/IRSTD_HC_Platform.
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            <a href="https://www.alphaxiv.org/abs/2512.03454v1" target="_blank" rel="noopener noreferrer">
                三思而后行：面向自动驾驶的世界模型启发的多模态接地
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            Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haicheng Liao, Huanming Shen, Bonan Wang, Yongkang Li, Yihong Tang, Chengyue Wan...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于自动驾驶领域，属于特定领域应用（自动驾驶），与您关注的推荐系统、搜索或广告核心领域无关。虽然涉及多模态和世界模型概念，但这些技术在该论文中被专门应用于自动驾驶场景，没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:14:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03454v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03454v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Interpreting natural-language commands to localize target objects is critical for autonomous driving (AD). Existing visual grounding (VG) methods for autonomous vehicles (AVs) typically struggle with ambiguous, context-dependent instructions, as they lack reasoning over 3D spatial relations and anticipated scene evolution. Grounded in the principles of world models, we propose ThinkDeeper, a framework that reasons about future spatial states before making grounding decisions. At its core is a Spatial-Aware World Model (SA-WM) that learns to reason ahead by distilling the current scene into a command-aware latent state and rolling out a sequence of future latent states, providing forward-looking cues for disambiguation. Complementing this, a hypergraph-guided decoder then hierarchically fuses these states with the multimodal input, capturing higher-order spatial dependencies for robust localization. In addition, we present DrivePilot, a multi-source VG dataset in AD, featuring semantic annotations generated by a Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT)-prompted LLM pipeline. Extensive evaluations on six benchmarks, ThinkDeeper ranks #1 on the Talk2Car leaderboard and surpasses state-of-the-art baselines on DrivePilot, MoCAD, and RefCOCO/+/g benchmarks. Notably, it shows strong robustness and efficiency in challenging scenes (long-text, multi-agent, ambiguity) and retains superior performance even when trained on 50% of the data.
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            <a href="https://www.alphaxiv.org/abs/2512.03453v1" target="_blank" rel="noopener noreferrer">
                GeoVideo：将几何正则化引入视频生成模型
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            GeoVideo: Introducing Geometric Regularization into Video Generation Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunpeng Bai, Shaoheng Fang, Chaohui Yu, Fan Wang, Qixing Huang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于视频生成模型，属于纯粹的视觉内容生成领域，与推荐系统、搜索或广告的排名核心任务无关。虽然标题提到“几何正则化”这一技术概念，但应用场景仅限于视频生成，没有表明对异构数据处理、序列建模或推荐系统架构有任何潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:11:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03453v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03453v1
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                    Recent advances in video generation have enabled the synthesis of high-quality and visually realistic clips using diffusion transformer models. However, most existing approaches operate purely in the 2D pixel space and lack explicit mechanisms for modeling 3D structures, often resulting in temporally inconsistent geometries, implausible motions, and structural artifacts. In this work, we introduce geometric regularization losses into video generation by augmenting latent diffusion models with per-frame depth prediction. We adopted depth as the geometric representation because of the great progress in depth prediction and its compatibility with image-based latent encoders. Specifically, to enforce structural consistency over time, we propose a multi-view geometric loss that aligns the predicted depth maps across frames within a shared 3D coordinate system. Our method bridges the gap between appearance generation and 3D structure modeling, leading to improved spatio-temporal coherence, shape consistency, and physical plausibility. Experiments across multiple datasets show that our approach produces significantly more stable and geometrically consistent results than existing baselines.
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            <a href="https://www.alphaxiv.org/abs/2512.03449v1" target="_blank" rel="noopener noreferrer">
                LM-CartSeg：用于影像组学分析的侧向和内侧软骨及软骨下骨自动分割
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            LM-CartSeg: Automated Segmentation of Lateral and Medial Cartilage and Subchondral Bone for Radiomics Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tongxu Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向医学影像分析领域（软骨和软骨下骨分割），属于明确的医学/生物学应用范畴。虽然涉及自动化分割技术，但没有任何迹象表明与推荐系统、搜索、广告或相关使能技术（如LLM、Transformer架构）存在关联。这完全属于被排除的“医学、生物学、化学、物理学或其他特定领域应用”类别。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 05:07:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03449v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03449v1
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Background and Objective: Radiomics of knee MRI requires robust, anatomically meaningful regions of interest (ROIs) that jointly capture cartilage and subchondral bone. Most existing work relies on manual ROIs and rarely reports quality control (QC). We present LM-CartSeg, a fully automatic pipeline for cartilage/bone segmentation, geometric lateral/medial (L/M) compartmentalisation and radiomics analysis. Methods: Two 3D nnU-Net models were trained on SKM-TEA (138 knees) and OAIZIB-CM (404 knees). At test time, zero-shot predictions were fused and refined by simple geometric rules: connected-component cleaning, construction of 10 mm subchondral bone bands in physical space, and a data-driven tibial L/M split based on PCA and k-means. Segmentation was evaluated on an OAIZIB-CM test set (103 knees) and on SKI-10 (100 knees). QC used volume and thickness signatures. From 10 ROIs we extracted 4 650 non-shape radiomic features to study inter-compartment similarity, dependence on ROI size, and OA vs. non-OA classification on OAIZIB-CM Results: Post-processing improved macro ASSD on OAIZIB-CM from 2.63 to 0.36 mm and HD95 from 25.2 to 3.35 mm, with DSC 0.91; zero-shot DSC on SKI-10 was 0.80. The geometric L/M rule produced stable compartments across datasets, whereas a direct L/M nnU-Net showed domain-dependent side swaps. Only 6 to 12 percent of features per ROI were strongly correlated with volume or thickness. Radiomics-based models models restricted to size-linked features. Conclusions: LM-CartSeg yields automatic, QCd ROIs and radiomic features that carry discriminative information beyond simple morphometry, providing a practical foundation for multi-centre knee OA radiomics studies.
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            <a href="https://www.alphaxiv.org/abs/2512.03445v1" target="_blank" rel="noopener noreferrer">
                基于多智能体数据生成的多方面知识增强医疗视觉-语言预训练
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            <i class="fa fa-star mr-1"></i>1/10
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            Multi-Aspect Knowledge-Enhanced Medical Vision-Language Pretraining with Multi-Agent Data Generation
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xieji Li, Siyuan Yan, Yingsheng Liu, H. Peter Soyer, Monika Janda, Victoria Mar,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确属于医疗领域应用，属于明确的无关主题。虽然标题包含'Vision-Language Pretraining'，但这是针对医疗领域的特定应用，没有展示对推荐系统、搜索或广告领域的潜在适用性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 04:55:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03445v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03445v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Vision-language pretraining (VLP) has emerged as a powerful paradigm in medical image analysis, enabling representation learning from large-scale image-text pairs without relying on expensive manual annotations. However, existing methods often struggle with the noise inherent in web-collected data and the complexity of unstructured long medical texts. To address these challenges, we propose a novel VLP framework integrating a Multi-Agent data GENeration (MAGEN) system and Ontology-based Multi-Aspect Knowledge-Enhanced (O-MAKE) pretraining. First, MAGEN enhances data quality by synthesizing knowledge-enriched descriptions via a foundation model-assisted captioning and retrieval-based verification pipeline. Second, O-MAKE addresses the difficulty of learning from long, unstructured texts by decomposing them into distinct knowledge aspects. This facilitates fine-grained alignment at both global and patch levels, while explicitly modeling medical concept relationships through ontology-guided mechanisms. We validate our framework in the field of dermatology, where comprehensive experiments demonstrate the effectiveness of each component. Our approach achieves state-of-the-art zero-shot performance on disease classification and cross-modal retrieval tasks across eight datasets. Our code and the augmented dataset Derm1M-AgentAug, comprising over 400k skin-image-text pairs, will be released at https://github.com/SiyuanYan1/Derm1M.
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            <a href="https://www.alphaxiv.org/abs/2512.03430v1" target="_blank" rel="noopener noreferrer">
                基于低层预训练扩散特征的光谱FiLM调制的标签高效高光谱图像分类
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            Label-Efficient Hyperspectral Image Classification via Spectral FiLM Modulation of Low-Level Pretrained Diffusion Features
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuzhen Hu, Biplab Banerjee, Saurabh Prasad
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于高光谱图像分类，这属于计算机视觉中的特定领域应用，与推荐系统、搜索或广告没有直接关联。尽管提到了扩散模型和调制技术，但应用场景仅限于遥感图像处理，没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 04:23:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03430v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03430v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Hyperspectral imaging (HSI) enables detailed land cover classification, yet low spatial resolution and sparse annotations pose significant challenges. We present a label-efficient framework that leverages spatial features from a frozen diffusion model pretrained on natural images. Our approach extracts low-level representations from high-resolution decoder layers at early denoising timesteps, which transfer effectively to the low-texture structure of HSI. To integrate spectral and spatial information, we introduce a lightweight FiLM-based fusion module that adaptively modulates frozen spatial features using spectral cues, enabling robust multimodal learning under sparse supervision. Experiments on two recent hyperspectral datasets demonstrate that our method outperforms state-of-the-art approaches using only the provided sparse training labels. Ablation studies further highlight the benefits of diffusion-derived features and spectral-aware fusion. Overall, our results indicate that pretrained diffusion models can support domain-agnostic, label-efficient representation learning for remote sensing and broader scientific imaging tasks.
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            <a href="https://www.alphaxiv.org/abs/2512.03427v1" target="_blank" rel="noopener noreferrer">
                基于无人机的林业应用中深度立体匹配方法的泛化能力评估
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            Generalization Evaluation of Deep Stereo Matching Methods for UAV-Based Forestry Applications
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard Green
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确指向计算机视觉中的立体匹配技术及其在林业领域的应用，属于纯粹的视觉应用论文。虽然涉及深度学习方法，但完全聚焦于特定领域（林业）的视觉任务，与推荐系统、搜索或广告的核心技术、LLM应用、Transformer架构进展或异构数据统一建模等当前关注领域均无直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-12-03 04:14:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2512.03427v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2512.03427v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Autonomous UAV forestry operations require robust depth estimation methods with strong cross-domain generalization. However, existing evaluations focus on urban and indoor scenarios, leaving a critical gap for specialized vegetation-dense environments. We present the first systematic zero-shot evaluation of eight state-of-the-art stereo methods--RAFT-Stereo, IGEV, IGEV++, BridgeDepth, StereoAnywhere, DEFOM (plus baseline methods ACVNet, PSMNet, TCstereo)--spanning iterative refinement, foundation model, and zero-shot adaptation paradigms. All methods are trained exclusively on Scene Flow and evaluated without fine-tuning on four standard benchmarks (ETH3D, KITTI 2012/2015, Middlebury) plus a novel 5,313-pair Canterbury forestry dataset captured with ZED Mini camera (1920x1080). Performance reveals scene-dependent patterns: foundation models excel on structured scenes (BridgeDepth: 0.23 px on ETH3D, 0.83-1.07 px on KITTI; DEFOM: 0.35-4.65 px across benchmarks), while iterative methods maintain cross-domain robustness (IGEV++: 0.36-6.77 px; IGEV: 0.33-21.91 px). Critical finding: RAFT-Stereo exhibits catastrophic ETH3D failure (26.23 px EPE, 98 percent error rate) due to negative disparity predictions, while performing normally on KITTI (0.90-1.11 px). Qualitative evaluation on Canterbury forestry dataset identifies DEFOM as the optimal gold-standard baseline for vegetation depth estimation, exhibiting superior depth smoothness, occlusion handling, and cross-domain consistency compared to IGEV++, despite IGEV++'s finer detail preservation.
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                                details.style.display = isExpanded ? 'none' : 'block';
                                
                                // 更新图标状态
                                this.className = isExpanded ? 
                                    'expand-icon fa fa-eye-slash cursor-pointer' : 'expand-icon fa fa-eye cursor-pointer';
                                this.style.marginRight = '8px';
                            }
                        });
                    }
                    
                    // 为标题元素添加点击事件，也可以展开/折叠，但会检查点击目标
                    titleElement.addEventListener('click', function(e) {
                        // 仅当点击的是标题本身（非链接、非图标）时才展开/折叠
                        if (!e.target.closest('a') && !e.target.closest('.expand-icon')) {
                            const details = paper.querySelector('.paper-details');
                            if (details) {
                                const isExpanded = details.style.display === 'block';
                                details.style.display = isExpanded ? 'none' : 'block';
                                
                                // 更新图标状态
                                const iconElement = this.querySelector('.expand-icon');
                                if (iconElement) {
                                    iconElement.className = isExpanded ? 
                                        'expand-icon fa fa-eye-slash cursor-pointer' : 'expand-icon fa fa-eye cursor-pointer';
                                    iconElement.style.marginRight = '8px';
                                }
                            }
                        }
                    });
                }
            });
            
            // 实现"仅显示精选"按钮功能
            const showSelectedButton = document.getElementById('show-selected');
            if (showSelectedButton) {
                showSelectedButton.addEventListener('click', function() {
                    // 显示所有精选论文，隐藏所有普通论文
                    const selectedPapers = document.querySelectorAll('.paper-card');
                    const normalPapers = document.querySelectorAll('.simple-paper-card');
                    
                    selectedPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    normalPapers.forEach(paper => {
                        paper.style.display = 'none';
                    });
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${selectedPapers.length} 篇论文 (共 ${selectedPapers.length + normalPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-all').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 隐藏展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) expandToggle.style.display = 'none';
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'none';
                });
            }
            
            // 实现"全部论文"按钮功能
            const showAllButton = document.getElementById('show-all');
            if (showAllButton) {
                showAllButton.addEventListener('click', function() {
                    // 显示所有论文
                    const allPapers = document.querySelectorAll('.paper-card, .simple-paper-card');
                    allPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    // 重置折叠状态
                    papersContainer.classList.remove('expanded-all');
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${allPapers.length} 篇论文 (共 ${allPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-selected').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 重新显示展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) {
                        expandToggle.style.display = 'block';
                        expandToggle.textContent = '展开全部非精选论文';
                    }
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'block';
                });
            }
        });
    </script>
    <script>
    
    // 初始化日历
    document.addEventListener('DOMContentLoaded', () => {
        try {
            console.log('Attempting to initialize calendar...');
            initCalendar();
        } catch (error) {
            console.error('Error initializing calendar:', error);
        }
    });
    
    // 日历初始化函数
    function initCalendar() {
        const toggleBtn = document.getElementById('date-picker-toggle');
        const datePicker = document.getElementById('date-picker');
        const calendarGrid = document.getElementById('calendar-grid');
        const prevMonthBtn = document.getElementById('prev-month');
        const nextMonthBtn = document.getElementById('next-month');
        const currentMonthEl = document.getElementById('current-month');
        const selectedDateText = document.getElementById('selected-date-text');
        
        // 当前显示的日期（从页面获取）
        const currentDateStr = document.getElementById('current-date').textContent.trim().replace(/^\d+年|月|日/g, '');
        const currentDate = new Date(currentDateStr);
        let displayYear = currentDate.getFullYear();
        let displayMonth = currentDate.getMonth();
        
        // 有论文数据的日期列表
        const availableDates = ["20251105","20251107","20251009","20251121","20251113","20251030","20251111","20251126","20251204","20251031","20251017","20251021","20251010","20251202","20251127","20251024","20251022","20251029","20251114","20251118","20251120","20251016","20251015","20251028","20251014","20251119","20251112","20251106","20251125","20251023"];
        
        // 尝试从localStorage恢复选择状态
        const savedDate = localStorage.getItem('selectedDate');
        const savedYear = localStorage.getItem('selectedYear');
        const savedMonth = localStorage.getItem('selectedMonth');
        
        // 确保页面加载时显示当前选中的日期
        // 修复持久化问题：确保每次加载都能正确恢复选中状态
        if (savedDate) {
            selectedDateText.textContent = savedDate;
            if (savedYear) displayYear = parseInt(savedYear);
            if (savedMonth) displayMonth = parseInt(savedMonth);
        } else {
            // 首次加载时，将当前页面日期保存到localStorage
            const currentPageDate = currentDateStr.replace(/\//g, '-');
            selectedDateText.textContent = currentPageDate;
            localStorage.setItem('selectedDate', currentPageDate);
            localStorage.setItem('selectedYear', currentDate.getFullYear().toString());
            localStorage.setItem('selectedMonth', currentDate.getMonth().toString());
        }
    
        // 切换日历显示状态
        toggleBtn.addEventListener('click', (e) => {
            e.stopPropagation();
            
            // 显式控制hidden类的添加和移除
            if (datePicker.classList.contains('hidden')) {
                // 显示日历 - 确保移除hidden类
                datePicker.classList.remove('hidden');
                renderCalendar();
            } else {
                // 隐藏日历
                datePicker.classList.add('hidden');
            }
        });
        
        // 点击其他区域关闭日历
        document.addEventListener('click', () => {
            if (!datePicker.classList.contains('hidden')) {
                datePicker.classList.add('hidden');
            }
        });
        
        // 阻止日历内部点击事件冒泡
        datePicker.addEventListener('click', (e) => {
            e.stopPropagation();
        });
        
        // 上月和下月按钮
        prevMonthBtn.addEventListener('click', () => {
            displayMonth--;
            if (displayMonth < 0) {
                displayMonth = 11;
                displayYear--;
            }
            renderCalendar();
        });
        
        nextMonthBtn.addEventListener('click', () => {
            displayMonth++;
            if (displayMonth > 11) {
                displayMonth = 0;
                displayYear++;
            }
            renderCalendar();
        });
        
        /**
         * 渲染日历
         */
        function renderCalendar() {
            // 清空日历网格
            calendarGrid.innerHTML = '';
            
            // 更新当前月份显示
            const monthNames = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'];
            currentMonthEl.textContent = displayYear + '年' + monthNames[displayMonth];
            
            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>